Using semantic and context features for answer summary extraction

Evi Yulianti, Ruey Cheng Chen, Falk Scholer, Mark Sanderson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)


We investigate the effectiveness of using semantic and context features for extracting document summaries that are designed to contain answers for non-factoid queries. The summarization methods are compared against state-of-the-art factoid question answering and query-biased summarization techniques. The accuracy of generated answer summaries are evaluated using ROUGE as well as sentence ranking measures, and the relationship between these measures are further analyzed. The results show that semantic and context features give significant improvement to the state-of-the-art techniques.

Original languageEnglish
Title of host publicationADCS 2016 - Proceedings of the 21st Australasian Document Computing Symposium
EditorsSarvnaz Karimi, Mark Carman
PublisherAssociation for Computing Machinery
Number of pages4
ISBN (Electronic)9781450348652
Publication statusPublished - 5 Dec 2016
Event21st Australasian Document Computing Symposium, ADCS 2016 - Caulfield, Australia
Duration: 6 Dec 20167 Dec 2016

Publication series

NameACM International Conference Proceeding Series


Conference21st Australasian Document Computing Symposium, ADCS 2016


  • Answer summaries
  • Non-factoid queries
  • Summarization


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