{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T04:29:24Z","timestamp":1729225764355,"version":"3.27.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685489","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>Table summarization is a crucial task aimed at condensing information from tabular data into concise and comprehensible textual summaries. However, existing approaches often fall short of adequately meeting users\u2019 information and quality requirements and tend to overlook the complexities of real-world queries. In this paper, we propose a novel method to address these limitations by introducing query-focused multi-table summarization. Our approach, which comprises a table serialization module, a summarization controller, and a large language model (LLM), utilizes textual queries and multiple tables to generate query-dependent table summaries tailored to users\u2019 information needs. To facilitate research in this area, we present a comprehensive dataset specifically tailored for this task, consisting of 4,909\u00a0query-summary pairs, each associated with multiple tables. Through extensive experiments using our curated dataset, we demonstrate the effectiveness of our proposed method compared to baseline approaches. Our findings offer insights into the challenges of complex table reasoning for precise summarization, contributing to the advancement of research in query-focused multi-table summarization.<\/jats:p>","DOI":"10.3233\/faia240951","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:46:40Z","timestamp":1729172800000},"source":"Crossref","is-referenced-by-count":0,"title":["QFMTS: Generating Query-Focused Summaries over Multi-Table Inputs"],"prefix":"10.3233","author":[{"given":"Weijia","family":"Zhang","sequence":"first","affiliation":[{"name":"Information Retrieval Lab (IRLab), University of Amsterdam"}]},{"given":"Vaishali","family":"Pal","sequence":"additional","affiliation":[{"name":"Information Retrieval Lab (IRLab), University of Amsterdam"}]},{"given":"Jia-Hong","family":"Huang","sequence":"additional","affiliation":[{"name":"Information Retrieval Lab (IRLab), University of Amsterdam"}]},{"given":"Evangelos","family":"Kanoulas","sequence":"additional","affiliation":[{"name":"Information Retrieval Lab (IRLab), University of Amsterdam"}]},{"given":"Maarten","family":"de Rijke","sequence":"additional","affiliation":[{"name":"Information Retrieval Lab (IRLab), University of Amsterdam"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240951","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:46:40Z","timestamp":1729172800000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240951"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240951","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}