文章基本信息

标题：Automatic Title Generation in Scientific Articles for Authorship Assistance: A Summarization Approach
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作者：Jan Wira Gotama Putra ; Masayu Leylia Khodra
期刊名称：Journal of ICT Research and Applications
印刷版ISSN：2337-5787
电子版ISSN：2338-5499
出版年度：2017
卷号：11
期号：3
页码：253-267
语种：English
出版社：Institut Teknologi Bandung
其他摘要：This paper presents a study on automatic title generation for scientific articles considering sentence information types known as rhetorical categories. A title can be seen as a high-compression summary of a document. A rhetorical category is an information type conveyed by the author of a text for each textual unit, for example: background, method, or result of the research. The experiment in this study focused on extracting the research purpose and research method information for inclusion in a computer-generated title. Sentences are classified into rhetorical categories, after which these sentences are filtered using three methods. Three title candidates whose contents reflect the filtered sentences are then generated using a template-based or an adaptive K-nearest neighbor approach. The experiment was conducted using two different dataset domains: computational linguistics and chemistry. Our study obtained a 0.109-0.255 F1-measure score on average for computer-generated titles compared to original titles. In a human evaluation the automatically generated titles were deemed ‘relatively acceptable’ in the computational linguistics domain and ‘not acceptable’ in the chemistry domain. It can be concluded that rhetorical categories have unexplored potential to improve the performance of summarization tasks in general.
其他关键词：adaptive K-nearest neighbor(AKNN);chemistry domain;computational linguistics domain;rhetorical categories;scientific article;summarization;title generation.