Answering complex questions using query-focused summarization technique

Yllias Chali, Shafiq R. Joty

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

2 Citations (Scopus)

Abstract

Unlike simple questions, complex questions cannot he answered by simply extracting named entities. These questions require inferencing and synthesizing information from multiple documents that can be seen as a kind of topic-oriented, informative multi-document summarization. In this paper, we have experimented with one empirical and two unsupervised statistical machine learning techniques: k-means and Expectation Maximization (EM), for computing relative importance of the sentences. The feature set includes different kinds of features: lexical, lexical semantic, cosine similarity, basic element, tree kernel based syntactic and shallow-semantic. A gradient descent local search technique is used to learn the optimal weights of the features. The effects of the different features are also shown for all the methods of generating summaries.

Original languageEnglish
Title of host publicationProceedings - 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08
Pages131-134
Number of pages4
DOIs
Publication statusPublished - 22 Dec 2008
Event20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08 - Dayton, OH, United States
Duration: 3 Nov 20085 Nov 2008

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volume2
ISSN (Print)1082-3409

Other

Other20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08
CountryUnited States
CityDayton, OH
Period3/11/085/11/08

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ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Computer Science Applications

Cite this

Chali, Y., & Joty, S. R. (2008). Answering complex questions using query-focused summarization technique. In Proceedings - 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08 (pp. 131-134). [4669765] (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI; Vol. 2). https://doi.org/10.1109/ICTAI.2008.84