Holes in the Outline : Subject-dependent Abstract Quality and its Implications for Scientific Literature Search

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Pysyväisosoite

http://hdl.handle.net/10138/313970

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Huang , C , Casey , A , Glowacka , D & Medlar , A 2019 , Holes in the Outline : Subject-dependent Abstract Quality and its Implications for Scientific Literature Search . in CHIIR '19 : Proceedings of the 2019 Conference on Human Information Interaction and Retrieval . ACM , New York , pp. 289-293 , ACM SIGIR Conference on Human Information Interaction and Retrieval , Glasgow , United Kingdom , 10/03/2019 . https://doi.org/10.1145/3295750.3298953

Julkaisun nimi: Holes in the Outline : Subject-dependent Abstract Quality and its Implications for Scientific Literature Search
Tekijä: Huang, Chien-yu; Casey, Arlene; Glowacka, Dorota; Medlar, Alan
Tekijän organisaatio: Department of Computer Science
Julkaisija: ACM
Päiväys: 2019
Kieli: eng
Sivumäärä: 5
Kuuluu julkaisusarjaan: CHIIR '19
ISBN: 978-1-4503-6025-8
DOI-tunniste: https://doi.org/10.1145/3295750.3298953
URI: http://hdl.handle.net/10138/313970
Tiivistelmä: Scientific literature search engines typically index abstracts instead of the full-text of publications. The expectation is that the abstract provides a comprehensive summary of the article, enumerating key points for the reader to assess whether their information needs could be satisfied by reading the full-text. Furthermore, from a practical standpoint, obtaining the full-text is more complicated due to licensing issues, in the case of commercial publishers, and resource limitations of public repositories and pre-print servers. In this article, we use topic modelling to represent content in abstracts and full-text articles. Using Computer Science as a case study, we demonstrate that how well the abstract summarises the full-text is subfield-dependent. Indeed, we show that abstract representativeness has a direct impact on retrieval performance, with poorer abstracts leading to degraded performance. Finally, we present evidence that how well an abstract represents the full-text of an article is not random, but is a consequence of style and writing conventions in different subdisciplines and can be used to infer an "evolutionary" tree of subfields within Computer Science.
Avainsanat: scientific literature search
topic models
term taxonomy
INFORMATION
113 Computer and information sciences
Vertaisarvioitu: Kyllä
Tekijänoikeustiedot: cc_by_nc
Pääsyrajoitteet: openAccess
Rinnakkaistallennettu versio: acceptedVersion


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