Intelligent Information Management, 2013, 5, 182-190
Published Online November 2013 (
Open Access IIM
Using AdaBoost Meta-Learning Algorithm for Medical
News Multi-Document Summarization
Mahdi Gholami Mehr
Department of Computer and Information Technology, Malek-Ashtar University of Technology, Tehran, Iran
Received September 26, 2013; revised October 25, 2013; accepted November 6, 2013
Copyright © 2013 Mahdi Gholami Mehr. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Automatic text summarization involves reducing a text document or a larger corpus of multiple documents to a short set
of sentences or paragraphs that convey the main meaning of the text. In this paper, we discuss about multi-document
summarization that differs from the single one in which the issues of compression, speed, redundancy and passage se-
lection are critical in the formation of useful summaries. Since the number and variety of online medical news make
them difficult for experts in the medical field to read all of the medical news, an automatic multi-document summariza-
tion can be useful for easy study of information on the web. Hence we propose a new approach based on machine
learning meta-learner algorithm called AdaBoost that is used for summarization. We treat a document as a set of sen-
tences, and the learning algorithm must learn to classify as positive or negative examples of sentences based on the
score of the sentences. For this learning task, we apply AdaBoost meta-learning algorithm where a C4.5 decision tree
has been chosen as the base learner. In our experiment, we use 450 pieces of news that are downloaded from different
medical websites. Then we compare our results with some existing approaches.
Keywords: Multi-Document Summarization; Machine Learning; Decision Trees; AdaBoost; C4.5; Medical Document
1. Introduction
Nowadays there are lots of online medical news on the
web and study of these huge amount of information is
not possible for experts in medical field [1]. Medical
information on the web such as news, articles, clinical
trial reports is an important source to help clinicians in
patient treatment. Usually, clinicians go through author-
written abstracts or summaries available in the medical
domain and then decide whether articles are relevant to
them for in-depth study. Since all types of medical arti-
cles do not come with authors written abstracts or sum-
maries. An automatic muti-document summarization can
be useful for help clinicians or medical students to find
their relevant information on the web.
Text summarization is the process to produce a con-
densed representation of the content of its input for hu-
man consumption. In existing categorization for summa-
rization with respect to the number of input documents,
the summarization is divided into two types, namely sin-
gle and multi documents. Automatic multi document
summarization refers to the production process of a
compressed summary of documents while the content,
readability, and cohesion are maintained [2]. Considering
further complexities of multi document summarization
than single document summarization, we face with some
challenges among them. The most significant ones are as
following [3]:
The rate of information redundancy is higher in a
group of subject-related texts;
The need for devoting great attention to the extraction
of unknown perspectives in the documents and cov-
ering all of them;
Difficulty of producing a highly readable summary
from documents that address the same subject from
different perspectives;
Difficulty of ordering extractive phrases for produc-
tion of the final summary.
In another categorization based on the type of sum-
mary, if the summarized text was obtained through ex-
tracting some phrases from the original text, the summa-
rization would be “extractive” or “selective”, and if the
text summary is generated after understanding the avail-
able content in the original text, the summarization
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would be “abstractive” [4]. Both types of summariza-
tions face different challenges. In extracting method,
main challenges are identifying important sentences in
the text, distinguishing and extracting key words, and
analyzing the main text with the purpose of preparing a
readable and coherent summary. In abstract method, first,
the main text must be understood; then based on the
meaning of the text, a meaningful summary is produced.
In this method, the main challenges are natural language
processing and the analysis of the meaning of the text
with the purpose of comprehension.
In this paper, we present a machine learning based
model for a sentence extraction based, Multi document,
and informative text summarization in the medical do-
main (This work is an improvement of the study pro-
posed in [5]). In our work, we approach automatic text
summarization as a supervised learning task. We treat a
document as a set of sentences, which must be classified
as positive or negative examples of sentences based on
the summary worthiness of sentences where a sentence is
represented by a feature set, which includes a number of
features used in the summarization literature and some
other features specific to the medical domain.
Thus, this summarization task can be formulated as the
classical machine-learning problem of learning from
examples. There are several unusual aspects to this clas-
sification problem. For example, the size of positive ex-
amples in the training set is relatively small compared to
the size of the entire training set because a summary size
is roughly less than one-fourth of the size of the source
document. It has been generally thought that a summary
should be no shorter than 15% and no longer than 35% of
the source text [6].
C4.5 is typically applied to more balanced class dis-
tributions. In our experiment, we found that AdaBoost
improves and performs significantly and uniformly well,
when combined with C4.5. In general, AdaBoost works
by repeatedly running a given weak learning algorithm
on various distributions over the training data, and then
combining the classifiers produced by the weak learner
into a single composite classifier. There seem to be two
separate reasons for the improvement in performance that
is achieved by boosting. The first and better understood
effect of boosting is that it generates a hypothesis whose
error on the training set is small by combining many hy-
potheses whose error may be large (but still better than
random guessing). It seems that boosting may be helpful
to learning problems having either of the following two
properties. The first property, which holds for many
real-world problems, is that the observed examples tend
to have varying degrees of hardness. For such problems,
the boosting algorithm tends to generate distributions that
concentrate on the harder examples, thus challenging the
weak learning algorithm to perform well on these harder
parts of the sample space. The second property is that the
learning algorithm is sensitive to changes in the training
examples so that significantly different hypotheses are
generated for different training sets.
For text summarization applications, we need to rank
sentences based on its summary scores. So, for the sen-
tence ranking, we have to follow a new methodology to
combine decisions (discuss in Section 4).
We adopted and designed ten features to characterize
sentences (taken as basic linguistic units) in the docu-
The paper is organized as follows. Section 2 provides
related work. In Section 3, we discuss how to extract and
use features. In Section 4, the summarization method has
been discussed. We present the evaluation and the ex-
perimental results in Section 5.
2. Related Work
In this section, we discuss about some previous works
that are used in text summarization. Radev et al. [7] sug-
gested a multi-document extracting summary maker that
extracts the sentences of the summary from several texts.
The extraction is based on center clusters. To increase
coherence, Harry Hilda [8] and Mitra [9] extracted para-
graphs instead of sentences from documents. Knight and
Marcu [10] presented two algorithms for sentence com-
pression which are based on “Noisy Channel” and “De-
cision Tree”. The input of the algorithm of “Decision
Tree” is a long sentence, and the output is supposed to be
a shorter sentence but with more meaningful content.
Barzilay et al. [11] presented an algorithm for the fusion
of information. This algorithm tries to combine similar
sentences of the documents to create a new sentence
based on “Language Generation Technology”. Although
this method simulates human behavior in summarization
process to some extent, it is heavily dependent on exter-
nal sources such as “Dependency Parser”, “Production
Rules”, and etc. Therefore, the portability of this method
is limited. In sentence extracting strategy, clustering is
introduced with the purpose of eliminating redundant
information which is due to using multi documents [12].
But this technique cannot solve the problem of redun-
dancy entirely because some sentences can be in more
than one cluster. To solve this problem some researchers
predefine the number of clusters, or determine a thresh-
old level for similarities. Even if the number of sentences
is predefined, it is probable that the sentences with high-
est score in clusters are not the best sentences. To solve
this problem GA algorithm [13] is used; in this algorithm
the favorable summary is chosen from a group of sum-
maries that are created by combined sentences obtained
from the main documents. Four properties which are
length of sentence, cover criterion, information criterion,
and similarity are used as fitness function of GA algo-
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rithm for summarization.
MEAD [6] a popular summarization system ranks
sentences based on its similarity to the centroid, position
in the text, similarity to the first sentence of the article
and length. It uses linear combination of features whose
values are normalized between 0 and 1 for sentence
ranking. Redundancy is removed by a variation of MMR
(Maximal Marginal Relevance) algorithm [14].
Some machine learning approaches to extractive sum-
marization have already been investigated. In [15] sen-
tence extraction is viewed as a Bayesian classification
task. To our knowledge, there are few attempts to use
machine learning algorithm for medical document sum-
marization task. Most of the researchers extend to the
medical domain the summarization techniques already
used in other domains. One of the projects in medical
domain is [16]. MiTAP (MITRE Text and Audio Proc-
essing) monitors infectious disease outbreaks or other
biological threats by monitoring multiple information
sources. The work presented in [17] exploits extractive
techniques, which ranks the extracted sentences accord-
ing to the so-called cluster signature of the document.
The abstracts and full texts from the Journal of the
American Medical Association were used for their ex-
periments. TRESTLE (Text Retrieval Extraction and
Summarization Technologies for Large Enterprises) is a
system, which produces single sentence summaries of
pharmaceutical newsletters [18]. TRESTLE generates
summaries by filling the templates by the Information
Extraction process. The system Helpful Med [19] helps
professional and advanced users to access medical in-
formation on the Internet and in medical related data-
bases. An ontology based summarization approach has
been proposed in [20]. A query based medical informa-
tion summarization system that exploits ontology knowl-
edge has been proposed in [21].
The work presented in [21] uses ontology to expand
query words and assigns scores to sentences based on
number of original keywords (query words) and expand-
ed keywords. Most recently a variation of lexical chain-
ing method [22] called bio-chain [23] is used in bio-
medical text summarization.
Compared to the above-mentioned approaches, we de-
velop a machine learning based model for medical do-
cument summarization that also exploits domain knowl-
3. Summarization Method
In extractive text summarization approach, the main task
is to identify sentences in a source text, which are rele-
vant to the users while simultaneously reducing informa-
tion redundancy. Sentences are scored based on a set of
features. The top-n highest scoring sentences in a text are
then extracted where n is an upper bound, which is de-
termined by the compression rate. Finally the selected
sentences are presented to the user in their order of ap-
pearance in the original source text [24].
The proposed summarization method consists of three
primary parts that shows in Figure 1.
The preprocessing task includes formatting the docu-
ment, removal of punctuation marks (except dots at the
sentence boundaries).
3.1. Using AdaBoost for Sentence Extraction
We apply a meta-learner called AdaBoost for sentence
extraction, where a C4.5 decision tree [25] has been cho-
sen as the base learner.
The boosting algorithm takes as input a training set of
m examples
S, ,
xyx y where xi is an
instance drawn from some space X and represented in
some manner (typically, a vector of attribute values), and
yi Є Y is the class label associated with xi. In this paper,
we always assume that the set of possible labels Y is of
finite cardinality k.
In addition, the boosting algorithm has access to an-
other unspecified learning algorithm, called the weak
learning algorithm, which is denoted generically as
Weak Learn. The boosting algorithm calls Weak Learn
repeatedly in a series of rounds. On round t, the booster
provides Weak Learn with a distribution Dt over the
training set S. In response, Weak Learn computes a
classifier or hypothesis ht: X Y which should correctly
classify a fraction of the training set that has large prob-
ability with respect to Dt. That is, the weak learner’s goal
is to find a hypothesis ht which minimizes the (training)
Pr t
tiDti i
hx y
 
. Note that this error is
measured with respect to the distribution Dt that was
provided to the weak learner. This process continues for
T rounds, and, at last, the booster combines the weak
hypotheses h1hT into a single final hypothesis hfin. Still
unspecified are: 1) the manner in which Dt is computed
on each round, and 2) how hfin is computed. Different
boosting schemes answer these two questions in different
ways. AdaBoost uses the simple rule shown in Figure 2.
The initial distribution D1 is uniform over S so D1 (i) =
1/m for all i. To compute distribution Dt+1 from Dt and
the last weak hypothesis ht, we multiply the weight of
example i by some number
0, 1
if ht classifies xi
correctly, and otherwise the weight is left unchanged.
The weights are then renormalized by dividing by the
normalization constant weights are then renormalized by
dividing by the normalization constant Zt. Effectively,
“easy” examples that are correctly classified by many of
the previous weak hypotheses get lower weight, and
“hard” examples which tend often to be misclassified get
higher weight. Thus, AdaBoost focuses the most weight
on the examples which seem to be hardest for Weak
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Document Pre
Sentence Extraction by
using AdaBoost and c4.5
Summary generation
Figure 1. Summarization method.
 
sequence of m examples
, ,...,,
with lables y1,...,
weak learning algorithm WeakLearn
integer T specifying number of iterations
D1 for all i
Algorithm AdaBoost
xyx ym
:( )
t = 1,2,...,T:
1) Call WekLearn, providing it with the distribution D .
2) Get back a hypothesis h:.
3) Calculate the error of h ()
If 12, set T = t-1 and abo
Do for
ti i
tt t
ih xy
  
rt loop.
4) Set 1.
5) Update distribution D:
if h
D1 otherwise
Where Z is a normalization constant
chosen so that D+1will be a distribution.
tt t
Di xy
 
the final hypothesis:
fin yY th xyt
Figure 2. The algorithm AdaBoost.
The number t
is computed as shown in the figure as
a function of t
. The final hypothesis hfin is a weighted
vote (i.e., a weighted linear threshold) of the weak hy-
potheses. That is, for a given instance x, hfin outputs the
label y that maximizes the sum of the weights of the
weak hypotheses predicting that label. The weight of
hypothesis ht is defined to be
log 1t
so that the
greater weight is given to hypotheses with lower error.
The important theoretical property about AdaBoost is
stated in the following theorem. This theorem shows that
if the weak hypotheses consistently have error only
slightly better than 1/2, then the training error of the final
hypothesis hfin drops to zero exponentially fast. For bi-
nary classification problems, this means that the weak
hypotheses need be only slightly better than random.
Theorem 1: suppose the weak learning algorithm
Weak Learn, when called by AdaBoost, generates hy-
potheses with errors 1,,
, where t
is as defined
in Figure 1. Assume each 12
and let
 .
Then the following upper bound holds on the error of
the final hypothesis hfin:
:14 exp 2
fin ii
ih xy
 
Theorem 1 implies that the training error of the final
hypothesis generated by AdaBoost is small. This does
not necessarily imply that the test error is small. How-
ever, if the weak hypotheses are “simple” and T “not too
large”, then the difference between the training and test
errors can also be theoretically bounded.
Traditionally, the component learners are of the same
general form. In our case, all component learners are
decision trees. In general, decision tree induction algo-
rithms have low bias but high variance.
Boosting multiple trees improves performance by re-
ducing variance and this procedure appears to have rela-
tively little impact on bias.
To train a learning algorithm, we need to establish a
set of features and a training corpus of document/extract
pairs. In our work, the main goal is to train a booster of
decision trees with the set of features and the multiple
versions of training set D and combine the decisions of
those trained decision trees to classify a sentence as
summary worthy (positive) or not (negative example).
After completion of training, the trained learning algo-
rithm is tested on unseen instances, which is not part of
training corpus.
3.1.1. Building Corpus
The training and test corpus are built by downloading
medical news form different websites. Then the summa-
ries are manually created for that news. A total of 450
news documents that downloaded from different web-
For each news, two manual summaries (model sum-
maries) are created by human abstractors. Since summa-
ries are very subjective and user sensitive, for each news
we decide to have three different model summaries cre-
ated by three different human abstractors. Human ab-
stractors are faculty members and postgraduate students
of our institute. Though human abstractors have been
instructed to create abstracts for each article by copying
material from the original text, they freely used their own
sentence construction while summarizing news.
But, to apply machine learning algorithm, we need to
have extracts instead of abstracts because for extracting
features for summary sentences we need to match the
manual summary sentences to the sentences in the origin-
al document. So, for each manually created abstract, we
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create an extract by selecting the sentences from the
original document that best match the sentences in the
abstract. The average size of an extract is 25% of the
source document. We choose relatively long medical
news documents in our study because the summarization
becomes more useful for long news documents. The rea-
son behind choosing medical news articles for our ex-
periment is that the medical news does not come with
any abstract or summaries. Though some features of the
medical news articles and general newspaper articles
may overlap, we found that the medical news have some
features such as medical terms, medical cue phrases
which may be absent in the general newspaper articles. It
is a common practice to evaluate a machine learning al-
gorithm using k-fold cross validation, where the entire
dataset is divided into k subsets, and each time, one of
the k subsets is used as the test set and the other k 1
subsets are put together to form a training set. Thus,
every data point gets to be in a test set exactly once, and
gets to be in a training set k 1 times. For the evaluation
of the proposed learning based system, the entire dataset
are divided into 2 folds where each fold consists of one
training set of 300 documents and a test set of 150
3.1.2. Featur es
To characterize the sentences in the medical documents
we have designed and adopted a number of features such
as: centroid overlap, similarity to subject, similarity of
sentences to each other, positive and negative cue phra-
ses, acronyms, sentence position, sentence length, nume-
ral data.
For normalization of each feature value, we divide the
value by the maximum of the scores obtained by the sen-
tences due to the feature. The following we discuss the
features in detail.
Centroid: this criterion is used to calculate the simi-
larity of the sentences to the central sentence of the
text. The following procedure is used to find the cen-
tral sentence of the text.
With the use of sentence similarity matrix, total
similarity of a sentence to the rest of the sentences
is calculated with the following method:
SimSenSimMat ,
SimMat [i,j] shows the similarity of ith sentence to jth
sentence. After calculation, maximum value for SimSeni
is found and the position of the sentence-sentence index
is placed in index-centroid variable.
With the use of cosine similarity formula, similar-
ity of each sentence to the center is calculated as
Centroid_SenSim ,
Then the above relation is normalized between 0 and
Similarity to title: A good summary consists of sen-
tences that are similar to the title [26,27]. This means
that if sentence Si is the most similar sentence to the
title, in comparison to other sentences in the docu-
ment, sentence Si can be considered more important
than other sentences or the most important sentence.
Calculating this criterion is as follows:
Title_Sen Sim,
Then the above relation is normalized between 0 and 1.
The more the value (closer to 1), the greater the similar-
ity is.
Sentence position: A summery which contains the
first and the last sentence of a document is a good
summary [28]. For this reason, the position of the
sentences in a text is very important. The following
formula is used to calculate the position of the sen-
tence in a document:
Pos_Score10.5 Sin1
DocRank ,
 
N is the total number of documents. DocRankj is the
parameter that is used for giving higher score to the first
and the last sentences which have more importance.
Therefore, documents need to be ranked. For ranking, the
number of keywords in each document is divided to the
total number of words in the document. The score of the
document with more keywords is closer to one while
score closer to zero represent fewer keywords.
Num of Keyword
DockRank maxNum of Keyword,
Positive and negative cue phrases in medical do-
main: A sentence gets score of n if it contains n posi-
tive cue phrases and gets score of n if it contains n
negative cue phrases from our knowledge base.
Acronyms: A sentence gets a score based on the
number of acronyms it contains. In medical articles,
authors frequently use acronyms for important com-
plex medical terms, perhaps it help them memorize
the things better. So, we consider acronym as an im-
portant feature for medical document summarization
task. If some letters (at least two letters) of a term are
capital, we treat the term as an acronym (gene names,
medical instruments etc.).
Sentence length long or short sentences are not suit-
able for the summary. Therefore, to calculate this cri-
terion, first the lengths of all sentences in the text are
calculated. Afterward the average length called Lenavg
is calculated. Now, the following formula is used to
score each sentence of the text for the sentence length
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avg avg
Sen_Len else
Len LenLen
Numerical data: Numerical data like date and time is
important in news. Hence a sentence gets score (+1) if
it contains numerical data.
3.1.3. Sentence Extrac ti on
Training a learning algorithm for summary sentence ex-
traction requires document sentences to be represented as
feature vectors. For this purpose, we write a computer
program for automatically extracting values for the fea-
tures characterizing the sentences in the documents. For
each sentence in the given document we extract the fea-
ture values from the source document using the measures
discussed in Sub-Section 3.1.2. If the sentence under
consideration is found in both the extracts, extract1 and
extract 2, which are created from the human abstracts
(discussed in 3.1.1), we label the sentence as “Summary
Worthy” sentence. If it is found in one of these extracts,
we label the sentence as “Moderately Summary Worthy”
and if it is not found in any one of these extracts we label
the sentence as “Summary Unworthy”. Thus each sen-
tence vector looks like {<a1 a2 a3 ··· an>, <label>} which
becomes an instance (example) for a base learner C4.5
decision tree, where a1, a2, ···, an, indicate feature values
for a sentence. All the documents in our corpus are con-
verted to a set of instances of the above form. We divide
the entire data set into 3 folds where each fold consists of
one training set corresponding to a set of training docu-
ments and a test set corresponding to a set of test docu-
ments. After preparation of a training set, the multiple
decision trees are trained with the different versions of
the training set and the decisions of those trained deci-
sion trees are combined to classify a sentence as one of
three categories: “Summary Worthy”, “Moderately Sum-
mary Worthy” and “Summary Unworthy”. For each fold,
a model is built from a training set using the boosting
technique and then the learned model is applied to the
test set. For our experiments, we have used Weka (www. machine learning tools. Ini-
tially, for each fold, we submit the training data set and
the test data set to Weka. Then we select the option
“boosting” under meta-classifier folder in Weka. We
chose J48 (Weka’s implementation of Quinlan’s C4.5
decision tree) as a base learner and set the number-
of-base learners to the default value which is 10.
Though all the attribute values of the instances in the
training and test sets are continuous, we did not apply
any separate discretization algorithm because C4.5 is
capable of handling continuous attribute values. We con-
figure WEKA in such a way that for each test instance,
we can get the predicted class and the probability esti-
mate for the class. The trained learning algorithm will
assign one of three labels: “Summary Worthy” (SW),
“Moderately Summary Worthy” (MSW), “Summary Un-
worthy” (SU) to a test instance corresponding to a sen-
tence in a test document. It is possible to save the output
in a separate file. We save the output produced by
WEKA in a file and then collect the classification output
for the sentences belonging to each test document. Then
we design a sentence-ranking algorithm based on the
classification output and the probability estimates for the
classes. The algorithm for sentence ranking is given be-
Sentence Ranking Algorithm
An output file produced by WEKA, which contains the
sentences of a test document with their classifications
and the probability estimates of the classes to which the
sentences belong.
Output: A file containing the ranked sentences
Read the input file.
Select those sentences, which have been classified as
“Summary Worthy” (SW) and reorder the selected
sentences in decreasing order of the probability esti-
mates of their classes. Save the selected sentences in
the output file and delete them from the input file.
Select those sentences, which have been classified as
“Moderately Summary Worthy” (MSW) and reorder
the selected sentences in decreasing order of the
probability estimates of their classes. Save the select-
ed sentences in the output file and delete them from
the input file.
For the rest of the sentences, which are classified as
“Summary Unworthy”, we order the sentences in in-
creasing order of the probability estimates of the class
labels. In effect, the sentence for which the probabil-
ity estimate is minimum (that is, the sentence is mi-
nimum “Summary Unworthy”) comes at the top. Ap-
pend the ordered sentences to the output file.
Close the output file.
End of the Algorithm
The sentence-ranking algorithm has three major steps.
At the first step, the sentences, which are classified as
“Summary Worthy”, we undoubtedly select those sen-
tences in the summary.
If the number of sentences selected at Step 1 is less
than the desired number of sentences, we consider those
sentences which are not selected in the summary at the
first step. At the second step, the sentences, which are
classified as “Moderately Summary Worthy”, are con-
sidered. If the number of sentences selected at Step 1 and
Step 2 are less than desired number of sentences, we con-
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sider the sentences, which have been classified as “Sum-
mary Unworthy” and order those sentences in increasing
order of the probability estimates of the class labels, that
is, the sentences are ordered from minimum summary
unworthiness (maximum summary worthiness) to maxi-
mum summary unworthiness (minimum summary wor-
thiness). They are selected in this order one by one in the
summary until the desired summary length is reached.
4. Summary Generation
After ranking the sentences, n top ranked sentences are
selected to generate the final summary. Value of n de-
pends on the compression rate. But, the summary pro-
duced in this way may contain some redundant informa-
tion, that is, some sentences in the summary may entail
partially or fully the concept embodied in other sentences.
This restricts the summary to be more informative when
the summary length is a restriction. Moreover, a user
who is used to just looking at first few sentences repre-
senting the same concept will prefer to see something
different information, though marginally less relevant.
To keep the sentences in the summary sufficiently dis-
similar from each other, the diversity based re-ranking
method called Maximal Marginal Relevance (MMR) is a
well-known measure. This approach uses a ranking pa-
rameter that allows the user to slide between relevance to
the query and diversity from the sentences seen so far.
The MMR algorithm is most suitable to apply in query-
focused summarization where the summary will be fo-
cused toward the user’s query. But in our generic sum-
marization environment where only one generic sum-
mary will be produced for a text document, we have used
a variant of the MMR algorithm to remove redundancy in
the summary. This algorithm works as follows:
Rank the sentences using the ranking algorithm dis-
cussed in Sub-Section 3.1.3.
Select the top ranked sentence first.
Select the next sentence from the ordered list and in-
clude into the summary if this sentence is sufficiently
dissimilar to all of the previously selected sentences.
Continue selecting sentences one by one until the pre-
defined summary length is reached.
The similarity between two sentences is measured us-
ing cosine similarity metric. If the cosine similarity be-
tween two sentences is greater (less) than a threshold, we
say that the sentences are similar (dissimilar). The cosine
similarity between two sentences is measured by the fol-
lowing formula as stated in [29].
Idf-modified-cosine ,
ii ii
xx xyyy
xx yy
tf tfidf
tf idftfidf
 
where ,S
is the number of occurrences of the word
in the sentence S, idf
inverse document fre-
quency of the word
and i
is the i-th word in the
sentence x and i
y is the i-th word in the sentence y. idf
value of a word is computed on a corpus of documents
using the formula: log(N/df) where N is the number of
documents in the corpus and df is the number of docu-
ments in the corpus that contain the word. Finally, the
sentences selected in the above-mentioned manner are
reordered using text order (sorted in the order in which
they appear in the input texts) to increase the readability
of the summary.
5. Experimental Results
To evaluate our summarization system, 450 medical news
articles have been downloaded from a number of online
medical news sources. From the downloaded articles, the
images and other links are manually removed and only
the news content is considered.
Traditionally, for each system generated summary,
more than one model summaries are used for evaluation
because the human abstractors may disagree with each
other in producing the summary of the document. But,
manual summary creation is a tedious task. In our ex-
periments, we have used two reference summaries for
evaluating a system generated summary.
For system evaluation, we have used precision and re-
Precision and recall: Precision and recall are the
well-known evaluation measures in the information re-
trieval settings. Since our system extracts sentences from
the source document to form a summary, we define pre-
cision and recall as follows:
Precision N
where, N = number of extracted sentences matched with
a reference summary and K = number of sentences ex-
tracted by the system.
Recall N
where, N = number of extracted sentences matched with
a reference summary and M = number of sentences in the
reference summary. Since we have used two reference
summaries for evaluating a system generated summary,
we have compared the system summary to each of the
reference summaries and computed the precision and re-
call. Thus for each system generated summary, we get
one pair of precision and recall values for the first refer-
ence summary and another pair of precision and recall
values for the second reference summary. We define the
average 12
R and the average 12
R as fol-
Open Access IIM
Average precision2
Average recall2
where 1
P= average precision of a system, where the
precision is computed by comparing the system gener-
ated summary and the first reference summary for a
document, 2
P= average precision of a system, where
the precision is computed by comparing the system gen-
erated summary and the second reference summary for a
document, 1
P= average recall of a system, where the
recall is computed by comparing the system generated
summary and the first reference summary for a document,
P= average recall of a system, where the recall is
computed by comparing the system generated summary
and the second reference summary for a document.
For evaluating the system using precision and recall,
we set the compression ratio to 15% and 20%. Compres-
sion ratio r% means r percent of the total sentences in the
source documents are extracted as a summary. To meas-
ure the overall performance of the proposed learning
based summarization system, our experimental dataset
consisting of 450 documents are divided into 3 folds for
3-fold cross validation where each fold contains two in-
dependent sets: a training set of 300 documents and a test
set of 150 documents. For each fold, a separate model is
built from 300 documents and the learned model is ap-
plied to the test set of 150 documents. Thus, for each task,
if we consider all three folds, we can get a summary for
each of 450 documents in our corpus. For other systems
such as MEAD and the lead baseline system (which sim-
ply takes the first n words or n sentences of the document)
and Bagging Method to which the proposed system is
compared, we run the systems on the entire 450 docu-
ments in our corpus to collect 450 summaries for each
Table 1 shows the results in terms of precision and re-
call for the compression ratio set to 15% and Table 2
shows the results for the compression ratio set to 20%.
By analyzing Table 1, we find that for 15% summary
generation task, the learning based system performs bet-
ter than the Bagging Method, lead baseline and MEAD,
but MEAD performs worse than the lead baseline and
Bagging method.
Table 2 shows that for 20% summary generation task,
MEAD performs better than the lead baseline whereas
the learning based system performs better than MEAD
and Bagging method.
6. Conclusion
This paper discusses a machine learning based model for
text summarization in medical domain. Most of previous
works on text summarization in the medical domain extends
Table 1. Precision and recall for 15% summary generation
task on the test data set.
Average PrecisionR1R2 Average RecallR1R2
Proposed Method0.69 0.26
Mead 0.54 0.24
Baseline-Lead 0.58 0.25
Bagging Method0.63 0.29
Table 2. Precision and recall for 20% summary generation
task on the test data set
Average PrecisionR1R2 Average RecallR1R2
Proposed Method0.61 0.34
Mead 0.54 0.31
Baseline-Lead 0.47 0.27
Bagging Method0.59 0.35
the various features used in other domains to the medical
domain. In our work, we have combined several medical
domain specific features with some other features used in
the state-of-art summarization approaches. A machine-
learning tool has been used for effective feature combi-
nation. The proposed approach performs better than the
systems it is compared to.
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