final3.txt

The normalized length of the sentence is calculated
as the ratio between a number of words in the sentence to the
number of words in the longest sentence in the document. P (s E< SII1,h,h, .... In)
represents the probability of the sentences to be included in
the summary based on the given features possessed by the
sentence. SENTENCE LEVEL FEATURES
2.1 Sentence location feature
The sentences that occur in the beginning and the conclusion
part of the document are most likely important since most
documents are hierarchically structured with important information
in the beginning and the end of the paragraphs. The probability of classification are learned from the
training data by the following Bayes rule [16]: where s represents
the set of sentences in the document and fi represents
the features used in classification stage and S represents the
set of sentences in the summary. The basic steps in concept
based summarization are: i) Retrieve concepts of a text from
IEEE International Conference on Computer, Communication, and Signal Processing (ICCCSP-2017)
external knowledge base(HowNet, WordNet, Wikipedia) ii)
Build a conceptual vector or graph model to depict relationship
between concept and sentences iii) Apply ranking algorithm to
score sentences iv) Generate summaries based on the ranking
scores of sentences
4. The sentences are restricted as a non-summary
and summary sentence based on the feature possessed by the
sentence. The first
step involves labeling the training data using a machinelearning
approach and then extract features of the sentences
in both test set and train sets which is then inputted to the
neural network system to rank the sentences in the document. 1.2 Title Word feature
The sentences in the original document which consists of
words mentioned in the title have greater chances to contribute
to the final summary since they serve as indicators of the theme
of the document. 1.5 Upper case word feature
The words which are in uppercase such as "UNICEF" are
considered to be important words and those sentences that
consist of these words are termed important in the context of
sentence selection for the final summary. The unsupervised approaches do not
need human summaries (user input) in deciding the important
features of the document, it requires the most sophisticated
algorithm to provide compensation for the lack of human
knowledge. The major drawback with the
supervised approach is that it requires known manually created
summaries by a human to label the sentences in the original
training document enclosed with "summary sentence" or "nonsummary
sentence" and it also requires more labeled training
data for classification. In the methodology proposed [12], the
importance of sentences is calculated based on the concepts
retrieved from HowNet instead of words. It is very
crucial for humans to understand and to describe the content
of the text. IEEE International Conference on Computer, Communication, and Signal Processing (ICCCSP-2017)
TABLE I
SUPERVISED AND UNSUPERVISED LEARNING METHODS FOR TEXT SUMMARIZATION
Categories Methodology Concept
SUPERVISED Machine Learning ap- Summarization task
LEARNING proach Bayes rule modelled as classification
APPROACHES problem
Trainable summarization -
SUPERVISED neural network is trained,
LEARNING Artificial Neural Net- pruned and generalized to
APPROACHES work filter sentences and classify
them as "summary" or
"non-summary sentence"
SUPERVISED Statistical modelling ap-
LEARNING Conditional Random proach which uses CRF as
APPROACHES Fields (CRF) a sequence labelling prob- lem
UNSUPERVISED Graph based Construction of graph to
LEARNING Approach capture relationship be-
APPROACHES tween sentences
Importance of sentences
UNSUPERVISED Concept oriented ap- calculated based on
LEARNING the concepts retrieved
APPROACHES proach from external knowledge
base(wikipedia, HowNet)
UNSUPERVISED Fuzzy Logic based ap- Summarization based on
LEARNING fuzzy rule using various
APPROACHES proach sets of features
Fig. IEEE International Conference on Computer, Communication, and Signal Processing (ICCCSP-2017)
1.3 Cue phrase feature
Cue phrases are words and phrases that indicate the structure
of the document flow and it is used as a feature in
sentence selection. The preprocessed
passage is sent to the feature extraction steps, which
is based on multiple features of sentences and words. LSA captures the text of the
input document and excerpt information such as words that
frequently occur together and words that are commonly seen in
different sentences. Example of Sentence concept bipartite graph proposed in [4]
Ladda Suanmali et al [11] proposed fuzzy logic approach
is used for automatic text summarization which is the initial
step , the text document is pre-processed followed by feature
extraction(Title features, Sentence length, Sentence position,
Sentence-sentence similarity, term weight, Proper noun and
Numerical data. The summary is generated by ordering the
ranked sentences in the order they occur in the original
document to maintain coherency. The sentences in the
document are represented as a graph and the edges between
the sentences represents weighted cosine similarity values. The significance
of sentences is strongly based on statistical and linguistic
features of sentences. Ramanathan
978-1-5090-3716-2/17/$31.00 ©2017 IEEE
Advantages Limitations
Large set of training data im- Human interruption required for proves the sentence selection for generating manual summaries summary
The network can be trained ac- I)Neural Network is slow in
cording to the style of human training phase and also in apreader. The approach specified
in [20]uses CRF as a sequence labelling problem and also
captures interaction between sentences through the features
extracted for each sentence and it also incorporates complex
features such as LSA_scores [21] and lilTS_score [22] but
the limitation is that linguistic features are not considered. In text Summarization, the most challenging task is
to summarize the contented from a number of semistructured
sources and textual, which includes web pages
and databases, in the proper way (size, format, time,
language,) for an explicit user. The major phase
is the feature fusion phase where the relationship between
the features are identified through two stages 1) eliminating
infrequent features 2) collapsing frequent features after
which sentence ranking is done to identify the important
summary sentences.Neural Network [17]after feature fusion
is depicted in Fig 8. The main problem in evaluation comes from
the impossibility of building a standard against which the
results of the systems that have to be compared. 2.4 Sentence-to-Sentence Cohesion
The cohesion between sentences for every sentence(s), the
similarity between s and alternative sentences are calculated
which are summed up and coarse value of the aspect is
obtained for s. The feature values are normalized between
[0, 1] where value closer to 1.0 indicates a higher degree of
cohesion between sentences. The main advantage of the method is that
it is able to identify correct features and provides a better
representation of sentences and groups terms appropriately
into its segments. The
scores obtained after the feature extraction are fed to the
neural network, which produces a single value as output score,
signifying the importance of the sentences. From Fig6 [13] it is to
be noted in order that dl is associated to d2 than dO and the
conversation 'walked' is linked to the talk 'man' but it is not
significant to the word 'park'. Neural network after training (a) and after pruning (b) [17]
In the approach proposed in [18], RankNet algorautomaticallyithm
using neural nets to identify the important sentences
in the document. Index Terms-Text Summarization, Unsupervised Learning,
Supervised Learning, Sentence Fusion, Extraction Scheme, Sentence
Revision, Extractive Summary
I. INTRODUCTION
In a recent advance, the significance of text summarization
[1] accomplishes more attention due to data inundation on
the web. However, the work has not focused the different
challenges of extractive text summarization process to its full
intensity in premises of time and space complication. Another graph based approach
LexRank [6], where the salience of the sentence is determined
by the concept of Eigen vector centrality. The sentence that consists of main keywords is most
likely included in the final summary. The main advantage
of a text summarization is reading time of the user can be
reduced. A conceptual vector
model is built to obtain a rough summarization and similarity
measures are calculated between the sentences to reduce
redundancy in the final summary. B. SUPERVISED LEARNING METHODS
Supervised extractive summarizationrelated techniques are
based on a classification approach at sentence level where
the system learns by examples to classify between summary
and non-summary sentences. FEATURES FOR EXTRACTIVE TEXT
SUMMARIZATION
Earlier techniques involve assigning a score to sentences
based on a countenance that are predefined based on the
methodology applied. The
sentences are clustered into groups based on their similarity
measures and then the sentences are ranked based on their
LexRank scores similar to PageRank algorithm [7]except that
the similarity graph is undirected in LexRank method. Dharmendra Hingu, Deep Shah and
Sandeep S.Udmale proposed an extractive approach [19]for
summarizing the Wikipedia articles by identifying the text
features and scoring the sentences by incorporating neural
network model [5]. Count(N-gram) is the number of N-grams in the set of
reference summaries. The implication of sentences is determined based on linguistic
and statistical features. The proposed system overcomes the issues faced
by non-negative matrix Factorization (NMF) methods by incorporating
conditional random fields (CRF) to identify and
extract correct features to determine the important sentence
of the given text.