A Novel Sentence Scoring Method for Extractive Text Summarization

Abstract

Saliency based sentence ranking is a basic step of extractive text summarization. Saliency of a sentence is often measured based on the important words that the sentence contains. One of the drawbacks of such saliency-based sentence extraction method is that it extracts mainly the sentences related to the most common topic in the document. But the input document may contain multiple topics or events and the users may like to see in the summary the salient information for each different topic or event. To alleviate such problem, diversity-based re-ranking approach or sentence clustering-based approach is commonly used. But re-ranking or sentence clustering makes the summarization process slow. In this paper, we propose a novel summarization method that computes the score of a sentence by combining saliency and novelty of the sentence. Without using any re-ranker or clustering of sentences, the proposed approach can automatically take care of the diversity issue while producing a summary. We have evaluated the performance of the system on DUC 2001 and DUC 2002 benchmark single document summarization datasets. Our experiments reveal that it outperforms several existing state-of-the-art extractive summarization approaches.