{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T18:37:23Z","timestamp":1771699043279,"version":"3.50.1"},"reference-count":42,"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>Valuable high-precision data are often published in the form of tables in both scientific and business documents. While humans can easily identify, interpret and contextualize tables, developing general-purpose automated techniques for extraction of information from tables is difficult due to the wide variety of table formats employed across corpora.<\/jats:p>\n          <jats:p>\n            To extract useful data from tables, data cells must be correctly extracted and linked to all relevant headers, units of measure and in-text references.\n            <jats:italic toggle=\"yes\">Table extraction<\/jats:italic>\n            involves identifying the border and cell structure for each document table, while\n            <jats:italic toggle=\"yes\">table understanding<\/jats:italic>\n            provides context by linking cells with semantic information inside and outside the table, such as row and column headers, footnotes, titles, and references in surrounding text.\n          <\/jats:p>\n          <jats:p>The objective of this tutorial is to provide a detailed synopsis of existing approaches for table extraction and understanding, highlight open research problems, and provide an overview of potential applications.<\/jats:p>","DOI":"10.14778\/3415478.3415563","type":"journal-article","created":{"date-parts":[[2020,9,14]],"date-time":"2020-09-14T18:46:40Z","timestamp":1600109200000},"page":"3433-3436","source":"Crossref","is-referenced-by-count":12,"title":["Table extraction and understanding for scientific and enterprise applications"],"prefix":"10.14778","volume":"13","author":[{"given":"Douglas","family":"Burdick","sequence":"first","affiliation":[{"name":"IBM Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marina","family":"Danilevsky","sequence":"additional","affiliation":[{"name":"IBM Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandre V","family":"Evfimievski","sequence":"additional","affiliation":[{"name":"IBM Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yannis","family":"Katsis","sequence":"additional","affiliation":[{"name":"IBM Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nancy","family":"Wang","sequence":"additional","affiliation":[{"name":"IBM Research"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,8]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Amazon. 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