Injecting Multiple Psychological Features into Standard Text Summarisers

Automatic Text Summarisation is an essential technology to cope with the overwhelming amount of documents that are daily generated. Given an information source, such as a webpage or a news article, text summarisation consists of extracting content from it and present it in a condensed form for human consumption. Summaries are crucial to facilitate information access. The reader is provided with the key information in a concise and fluent way. This speeds up navigation through large repositories of data. With the rapid growth of online contents, creating manual summaries is not an option. Extractive summarisation methods are based on selecting the most important sentences from the input. To meet this aim, a ranking of candidate sentences is often built from a reduced set of sentence features. In this paper, we show that that many features derived from psychological studies are valuable for constructing extractive summaries. These features encode psychological aspects of communication and are a good guidance for selecting salient sentences. We use Quantitative Text Analysis tools for extracting these features and inject them into state-of-the-art extractive summarisers. Incorporating these novel components into existing extractive summarisers requires to combine and weight a high number of sentence features. In this respect, we show that Particle Swarm Optimisation is a viable approach to set the feature's weights. Following standard evaluation practice (DUC benchmarks), we also demonstrate that our novel summarisers are highly competitive.

keywords: Summarisation, Psychological features, Linguistic Inquiry and Word Count, Particle Swarm Optimisation