TY - GEN
T1 - Utility of Missing Concepts in Query-biased Summarization
AU - Sarwar, Sheikh Muhammad
AU - Moraes, Felipe
AU - Jiang, Jiepu
AU - Allan, James
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - query-biased summarization
KW - topic modeling
UR - http://www.scopus.com/inward/record.url?scp=85111652663&partnerID=8YFLogxK
U2 - 10.1145/3404835.3463121
DO - 10.1145/3404835.3463121
M3 - Conference contribution
AN - SCOPUS:85111652663
T3 - SIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
SP - 2056
EP - 2060
BT - SIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
PB - Association for Computing Machinery (ACM)
T2 - 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2021
Y2 - 11 July 2021 through 15 July 2021
ER -