Article ID Journal Published Year Pages File Type
386782 Expert Systems with Applications 2014 12 Pages PDF
Abstract

Automatic titling of text documents is an essential task for several applications (automatic heading of e-mails, summarization, and so forth). This paper describes a system facilitating information retrieval in a set of textual documents by tackling the automatic titling and subtitling issue. Automatic titling here involves providing both informative and catchy titles. We thus propose two different approaches based on NLP, text mining, and Web Mining techniques. The first one (POSTIT) consists of extracting relevant noun phrases from texts as candidate titles. An original approach combining statistical criteria and noun phrase positions in the text helps in collecting informative titles and subtitles. The second approach (NOMIT) is based on various assumptions made on POSTIT and aims to generate both informative and catchy titles. Both approaches are applied to a corpus of news articles, then evaluated according to two criteria, i.e. informativeness and catchiness.

•We propose a process that automatically reformulate phrases to be used as titles.•We introduce the issue of catchiness.•We present a typology of titles adapted to automatic titling task.•We present two different automatic titling approaches.•This is the first time the catchiness of titles has been evaluated.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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