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Generating Text Summaries through the Relative Importance of Topics

Joel Larocca NetoAlexandre SantosCelso A. A. KaestnerAlex Alves Freitas

This work proposes a new extractive text-summarization algorithm based on the importance of the topics contained in a document. The basic ideas of the proposed algorithm are as follows. At first the document is partitioned by using the TextTiling algorithm, which identifies topics (coherent segments of text) based on the TF-IDF metric. Then for each topic the algorithm computes a measure of its relative relevance in the document. This measure is computed by using the notion of TF-ISF (Term Frequency - Inverse Sentence Frequency), which is our adaptation of the well-known TF-IDF (Term Frequency - Inverse Document Frequency) measure in information retrieval. Finally, the summary is generated by selecting from each topic a number of sentences proportional to the importance of that topic.

http://www.springerlink.com/content/k02cbn683nra1hgg/

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