Term-based Identification of Sentences for Text Summarisation

Abstract

The present paper describes a methodology for automatic text summarisation of Greek texts which combines terminology extraction and sentence spotting. Since generating abstracts has proven a hard NLP task of questionable effectiveness, the paper focuses on the production of a special kind of abstracts, called extracts: sets of sentences taken from the original text. These sentences are selected on the basis of the amount of information they carry about the subject content. The proposed, corpus-based and statistical approach exploits several heuristics to determine the summary-worthiness of sentences. It actually uses statistical occurrences of terms (TF· IDF formula) and several cue phrases to calculate sentence weights and then extract the top scoring sentences which form the extract.

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