Text summarisation in progress: a literature review

Abstract

This paper contains a large literature review in the research field of Text Summarisation (TS) based on Human Language Technologies (HLT). TS helps users manage the vast amount of information available, by condensing documents' content and extracting the most relevant facts or topics included in them. The rapid development of emerging technologies poses new challenges to this research field, which still need to be solved. Therefore, it is essential to analyse its progress over the years, and provide an overview of the past, present and future directions, highlighting the main advances achieved and outlining remaining limitations. With this purpose, several important aspects are addressed within the scope of this survey. On the one hand, the paper aims at giving a general perspective on the state-ofthe-art, describing the main concepts, as well as different summarisation approaches, and relevant international forums. Furthermore, it is important to stress upon the fact that the birth of new requirements and scenarios has led to new types of summaries with specific purposes (e.g. sentiment-based summaries), and novel domains within which TS has proven to be also suitable for (e.g. blogs). In addition, TS is successfully combined with a number of intelligent systems based on HLT (e.g. information retrieval, question answering, and text classification). On the other hand, a deep study of the evaluation of summaries is also conducted in this paper, where the existing methodologies and systems are explained, as well as new research that has emerged concerning the automatic evaluation of summaries' quality. Finally, some thoughts about TS in general and its future will encourage the reader to think of novel approaches, applications and lines to conduct research in the next years. The analysis of these issues allows the reader to have a wide and useful background on the main important aspects of this research field.

The challenging task of summary evaluation: an overview

“…In the context of text summarization, there is a great variety of summary categorizations according to different issues (Sparck Jones and Galliers, 1996;Lloret and Palomar, 2012). The traditional and most common ones include the type of input and output (e.g., single-or multi-document summaries; extractive or abstractive summaries; headlines).…”

Automatic Arabic Text Summarization Approaches

“…According to [26], a good summarization technology aims to combine the central topics with completeness, readability and conciseness. Determining the effective features that extract the main ideas from the input document and that cover all important themes is a greater challenge in extractive text summarization especially for Arabic language which have a complex nature and rich semantic.…”

Modelling the ‘hurried’ bug report reading process to summarize bug reports

“…The low recall, however, suggests that the summarizers extract sentences that are redundant: sentences that are present in different golden summaries, but that convey similar information. To improve on these results and avoid extracting redundant sentences, one could incorporate one of the many techniques from summarization, such as removing sentences subsumed by other sentences [17].…”