Graph-based biomedical text summarization: An itemset mining and sentence clustering approach

Sentence Clustering-based Summarization of Multiple Text Documents

With the rapid growth of the World Wide Web, information overload is becoming a problem for an increasingly large number of people. Automatic Multidocument summarization can be an indispensable solution to reduce the information overload problem on the web. This kind of summarization facility helps users to see at a glance what a collection is about and provides a new way of managing a vast hoa...

Sentence-based Text Summarization : Modelling and Evaluation

This paper addresses the problem of creating a summary by extracting a set of sentences that are likely to represent the content of a document. A small scale experiment is conducted leading to the compilation of an evaluation corpus for the Greek language. Three models of sentence extraction are then described, along the lines of shallow linguistic analysis, feature combination and machine lear...

Quantifying the informativeness for biomedical literature summarization: An itemset mining method

OBJECTIVE Automatic text summarization tools can help users in the biomedical domain to access information efficiently from a large volume of scientific literature and other sources of text documents. In this paper, we propose a summarization method that combines itemset mining and domain knowledge to construct a concept-based model and to extract the main subtopics from an input document. Our ...

Personalizing Text Summarization Based on Sentence Weighting

The amount of data that exists on the Internet is enormous enough to distract users when trying to find useful information. In addition, the expansive use of small screen devices for browsing the World Wide Web generates huge problems when trying to find and read information. A solution to these problems is to personalize the web and try to reduce algorithmically the amount of text. Many text s...

An Additive FAHP based sentence score function for text summarization