Utility of Missing Concepts in Query-biased Summarization

Sheikh Muhammad Sarwar, Felipe Moraes, Jiepu Jiang, James Allan

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Query-biased Summarization (QBS) aims to produce a query-dependent summary of a retrieved document to reduce the human effort for inspecting the full-text content. Typical summarization approaches extract document snippets that overlap with the query and show them to searchers. Such QBS methods show relevant information in a document but do not inform searchers what is missing. Our study focuses on reducing user effort in finding relevant documents by exposing the information in the query that is missing in the retrieved results. We use a classical approach, DSPApprox, to find terms or phrases relevant to a query. Then, we identify which terms or phrases are missing in a document, present them in a search interface, and ask crowd workers to judge document relevance based on snippets and missing information. Experimental results show both benefits and limitations of our method compared with traditional ones that only show relevant snippets.

Original languageEnglish
Title of host publicationSIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery (ACM)
Pages2056-2060
Number of pages5
ISBN (Electronic)9781450380379
DOIs
Publication statusPublished - 2021
Event44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2021 - Virtual, Online, Canada
Duration: 11 Jul 202115 Jul 2021

Publication series

NameSIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2021
CountryCanada
CityVirtual, Online
Period11/07/2115/07/21

Keywords

  • query-biased summarization
  • topic modeling

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