Similarity-Based Query-Focused Multi-document Summarization Using Crowdsourced and Manually-built Lexical-Semantic Resources

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Authors

Colleges, School and Institutes

Abstract

In this paper we present an approach for an extractive query focused multi-document summarization which stands on an enhanced knowledge-based short text semantic similarity measures. We incorporate WordNet Taxonomy with Categorial Variation Database (CatVar) and Morphosemantic Links to determine query similarity with sentences and intra-sentences similarities. Besides, we enrich WordNet-derived similarity with named entity semantic relatedness inferred from Wikipedia and underpinned by Normalized Google Distance. We show that our summarizer built primarily on such an improved semantic similarity measure to model relevance, centrality and diversity factors outperforms the best-performing relevant DUC systems and recent closely related studies in at least one or more of the investigated ROUGE metrics. An anti-redundancy mechanism is augmented with the proposed summarizer design using Maximum Marginal Relevance algorithm -MMR.

Details

Original languageEnglish
Title of host publicationProceedings 13th IEEE International Symposium on Parallel and Distributed Processing with Applications
Subtitle of host publicationISPA 2015
Publication statusPublished - Aug 2015
Event13th IEEE International Symposium on Parallel and Distributed Processing with Applications - Finland, Helsinki, Finland
Duration: 20 Aug 201522 Aug 2015

Conference

Conference13th IEEE International Symposium on Parallel and Distributed Processing with Applications
CountryFinland
CityHelsinki
Period20/08/1522/08/15