Automatic Title Generation in Scientific Articles for Authorship Assistance: A Summarization Approach
DOI:
https://doi.org/10.5614/itbj.ict.res.appl.2017.11.3.3Keywords:
adaptive K-nearest neighbor(AKNN), chemistry domain, computational linguistics domain, rhetorical categories, scientific article, summarization, title generation.Abstract
This paper presents a studyon automatic title generation for scientific articles considering sentence information types known as rhetorical categories. A title can be seenas 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 studyfocused on extracting the research purpose and research method information for inclusion in a computer-generated title. Sentences are classifiedinto rhetorical categories, after which these sentences are filtered using three methods. Three title candidates whose contents reflect the filtered sentencesare 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.
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