Unsupervised approach for selecting sentences in query-based summarization

Yllias Chali, Shafiq Rayhan Joty

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

7 Citations (Scopus)

Abstract

When a user is served with a ranked list of relevant documents by the standard document search engines, his search task is usually not over. He has to go through the entire document contents to judge its relevance and to find the precise piece of information he was looking for. Query-relevant summarization tries to remove the onus on the end-user by providing more condensed and direct access to relevant information. Query-relevant summarization is the task to synthesize a fluent, well-organized summary of the document collection that answers the user questions. We extracted several features of different types (i.e. lexical, lexical semantic, statistical and cosine similarity) for each of the sentences in the document collection in order to measure its relevancy to the user query. We experimented with two well-known unsupervised statistical machine learning techniques: K-Means and EM algorithms and evaluated their performances. For all these methods of generating summaries, we have shown the effects of different kinds of features.

Original languageEnglish
Title of host publicationProceedings of the 21th International Florida Artificial Intelligence Research Society Conference, FLAIRS-21
Pages47-52
Number of pages6
Publication statusPublished - 17 Nov 2008
Externally publishedYes
Event21th International Florida Artificial Intelligence Research Society Conference, FLAIRS-21 - Coconut Grove, FL, United States
Duration: 15 May 200817 May 2008

Other

Other21th International Florida Artificial Intelligence Research Society Conference, FLAIRS-21
CountryUnited States
CityCoconut Grove, FL
Period15/5/0817/5/08

Fingerprint

Search engines
Learning systems
Semantics

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

Cite this

Chali, Y., & Rayhan Joty, S. (2008). Unsupervised approach for selecting sentences in query-based summarization. In Proceedings of the 21th International Florida Artificial Intelligence Research Society Conference, FLAIRS-21 (pp. 47-52)

Unsupervised approach for selecting sentences in query-based summarization. / Chali, Yllias; Rayhan Joty, Shafiq.

Proceedings of the 21th International Florida Artificial Intelligence Research Society Conference, FLAIRS-21. 2008. p. 47-52.

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

Chali, Y & Rayhan Joty, S 2008, Unsupervised approach for selecting sentences in query-based summarization. in Proceedings of the 21th International Florida Artificial Intelligence Research Society Conference, FLAIRS-21. pp. 47-52, 21th International Florida Artificial Intelligence Research Society Conference, FLAIRS-21, Coconut Grove, FL, United States, 15/5/08.
Chali Y, Rayhan Joty S. Unsupervised approach for selecting sentences in query-based summarization. In Proceedings of the 21th International Florida Artificial Intelligence Research Society Conference, FLAIRS-21. 2008. p. 47-52
Chali, Yllias ; Rayhan Joty, Shafiq. / Unsupervised approach for selecting sentences in query-based summarization. Proceedings of the 21th International Florida Artificial Intelligence Research Society Conference, FLAIRS-21. 2008. pp. 47-52
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