{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T22:49:51Z","timestamp":1762296591001},"reference-count":0,"publisher":"Cambridge University Press (CUP)","issue":"2-3","license":[{"start":{"date-parts":[[2002,8,21]],"date-time":"2002-08-21T00:00:00Z","timestamp":1029888000000},"content-version":"unspecified","delay-in-days":81,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Nat. Lang. Eng."],"published-print":{"date-parts":[[2002,6]]},"abstract":"<jats:p>This paper describes a simple discourse parsing and analysis algorithm that combines a formal \nunderspecification utilising discourse grammar with Information Retrieval (IR) techniques. \nFirst, linguistic knowledge based on discourse markers is used to constrain a totally underspecified discourse representation. Then, the remaining underspecification is further specified \nby the computation of a topicality score for every discourse unit. This computation is done via \nthe vector space model. Finally, the sentences in a prominent position (e.g. the first sentence \nof a paragraph) are given an adjusted topicality score. The proposed algorithm was evaluated \nby applying it to a text summarisation task. Results from a psycholinguistic experiment, \nindicating the most salient sentences for a given text as the \u2018gold standard\u2019, show that the \nalgorithm performs better than commonly used machine learning and statistical approaches \nto summarisation.<\/jats:p>","DOI":"10.1017\/s1351324902002905","type":"journal-article","created":{"date-parts":[[2002,8,26]],"date-time":"2002-08-26T21:14:38Z","timestamp":1030396478000},"page":"235-255","source":"Crossref","is-referenced-by-count":14,"title":["Robust discourse parsing \nvia discourse markers, topicality and position"],"prefix":"10.1017","volume":"8","author":[{"given":"FRANK","family":"SCHILDER","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2002,8,21]]},"container-title":["Natural Language Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S1351324902002905","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,4,5]],"date-time":"2019-04-05T14:57:40Z","timestamp":1554476260000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S1351324902002905\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2002,6]]},"references-count":0,"journal-issue":{"issue":"2-3","published-print":{"date-parts":[[2002,6]]}},"alternative-id":["S1351324902002905"],"URL":"https:\/\/doi.org\/10.1017\/s1351324902002905","relation":{},"ISSN":["1351-3249","1469-8110"],"issn-type":[{"value":"1351-3249","type":"print"},{"value":"1469-8110","type":"electronic"}],"subject":[],"published":{"date-parts":[[2002,6]]}}}