{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T22:26:15Z","timestamp":1768256775601,"version":"3.49.0"},"reference-count":11,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2020,8]]},"abstract":"<jats:p>\n            Text document summarization refers to the task of producing a brief representation of a document for easy human consumption. Existing text summarization techniques mostly focus on generic summarization, but users often require\n            <jats:italic toggle=\"yes\">personalized<\/jats:italic>\n            summarization that targets their specific preferences and needs. However, precisely expressing preferences is challenging, and current methods are often ambiguous, outside the user's control, or require costly training data. We propose a novel and effective way to express summarization intent (preferences) via\n            <jats:italic toggle=\"yes\">examples<\/jats:italic>\n            : the user provides a few example summaries for a small number of documents in a collection, and the system summarizes the rest. We demonstrate SuDocu, an example-based personalized Document Summarization system. Through a simple interface, SuDocu allows the users to provide example summaries, learns the summarization intent from the examples, and produces summaries for new documents that reflect the user's summarization intent. SuDocu further explains the captured summarization intent in the form of a\n            <jats:italic toggle=\"yes\">package query<\/jats:italic>\n            , an extension of a traditional SQL query that handles complex constraints and preferences over answer sets. SuDocu combines topic modeling, semantic similarity discovery, and in-database optimization in a novel way to achieve example-driven document summarization. We demonstrate how SuDocu can detect complex summarization intents from a few example summaries and produce accurate summaries for new documents effectively and efficiently.\n          <\/jats:p>","DOI":"10.14778\/3415478.3415494","type":"journal-article","created":{"date-parts":[[2020,9,14]],"date-time":"2020-09-14T18:46:35Z","timestamp":1600109195000},"page":"2861-2864","source":"Crossref","is-referenced-by-count":3,"title":["SuDocu"],"prefix":"10.14778","volume":"13","author":[{"given":"Anna","family":"Fariha","sequence":"first","affiliation":[{"name":"University of Massachusetts"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matteo","family":"Brucato","sequence":"additional","affiliation":[{"name":"University of Massachusetts"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter J.","family":"Haas","sequence":"additional","affiliation":[{"name":"University of Massachusetts"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandra","family":"Meliou","sequence":"additional","affiliation":[{"name":"University of Massachusetts"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,8]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"http:\/\/sudocu.cs.umass.edu\/demo\/","year":"2020","unstructured":"Sudocu: http:\/\/sudocu.cs.umass.edu\/demo\/, 2020."},{"key":"e_1_2_1_2_1","first-page":"482","volume-title":"EMNLP","author":"Aker A.","year":"2010","unstructured":"A. Aker, T. Cohn, and R. Gaizauskas. Multi-document summarization using A* search and discriminative training. In EMNLP, pages 482--491, 2010."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.5555\/944919.944937"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.14778\/2904483.2904489"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.14778\/3342263.3342266"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.3115\/1620754.1620807"},{"key":"e_1_2_1_7_1","first-page":"912","volume-title":"NAACL","author":"Lin H.","year":"2010","unstructured":"H. Lin and J. Bilmes. Multi-document summarization via budgeted maximization of submodular functions. In NAACL, pages 912--920, 2010."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/DEXA.2012.47"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2484028.2484052"},{"key":"e_1_2_1_10_1","volume-title":"Neural abstractive text summarization with sequence-to-sequence models. arXiv preprint arXiv:1812.02303","author":"Shi T.","year":"2018","unstructured":"T. Shi, Y. Keneshloo, N. Ramakrishnan, and C. K. Reddy. Neural abstractive text summarization with sequence-to-sequence models. arXiv preprint arXiv:1812.02303, 2018."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/1502650.1502656"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3415478.3415494","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T02:28:14Z","timestamp":1758076094000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3415478.3415494"}},"subtitle":["summarizing documents by example"],"short-title":[],"issued":{"date-parts":[[2020,8]]},"references-count":11,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2020,8]]}},"alternative-id":["10.14778\/3415478.3415494"],"URL":"https:\/\/doi.org\/10.14778\/3415478.3415494","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2020,8]]}}}