Generic summarization and keyphrase extraction using mutual reinforcement principle and sentence clustering

Abstract

A novel method for simultaneous keyphrase extraction and generic text summarization is proposed by modeling text documents as weighted undirected and weighted bipartite graphs. Spectral graph clustering algorithms are used for partitioning sentences of the documents into topical groups with sentence link priors being exploited to enhance clustering quality. Within each topical group, saliency scores for keyphrases and sentences are generated based on a mutual reinforcement principle. The keyphrases and sentences are then ranked according to their saliency scores and selected for inclusion in the top keyphrase list and summaries of the document. The idea of building a hierarchy of summaries for documents capturing different levels of granularity is also briefly discussed. Our method is illustrated using several examples from news articles, news broadcast transcripts and web documents.