Text Summarization Techniques: A Brief Survey

In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.Comment: Some of references format have update

A Supervised Approach to Extractive Summarisation of Scientific Papers

Automatic summarisation is a popular approach to reduce a document to its main arguments. Recent research in the area has focused on neural approaches to summarisation, which can be very data-hungry. However, few large datasets exist and none for the traditionally popular domain of scientific publications, which opens up challenging research avenues centered on encoding large, complex documents. In this paper, we introduce a new dataset for summarisation of computer science publications by exploiting a large resource of author provided summaries and show straightforward ways of extending it further. We develop models on the dataset making use of both neural sentence encoding and traditionally used summarisation features and show that models which encode sentences as well as their local and global context perform best, significantly outperforming well-established baseline methods.Comment: 11 pages, 6 figure

Literature review writing: a study of information selection from cited papers / Kokil Jaidka, Christopher Khoo and Jin-Cheon Na

This paper reports the results of a small study of how researchers select and edit research information from cited papers to include in a literature review. This is part of a bigger content analysis and linguistic analysis of literature reviews. This study aims to answer the following questions: where do authors select information from the cited papers (e.g., Abstract, Introduction, Conclusion section, etc.)? What types of information do they select (e.g., research objectives, results, etc.), and How do they transform that information (e.g., paraphrasing, cut-pasting, etc.)? In order to answer these questions, we analyzed the literature review section of 20 articles from the Journal of the American Society for Information Science & Technology, 2001-2008, to answer these questions. Referencing sentences were mapped to source papers to determine their origin. Other features of the source information were also annotated, such as the type of information selected and the types of editing changes made to it before including into the literature review. Preliminary results indicate that authors prefer to select information from the Abstract, Introduction and Conclusion sections of the cited papers. This information is transformed through cut-paste, paraphrase or higher-level semantic transformations to describe the research objective, methodology and results of the referenced study. The choices made in selecting and transforming the source information appeared to be related to the two styles of literature review finally constructed – integrative and descriptive literature reviews. Keywords: Literature reviews; Multi-document summarization; Information science; Information extraction; Information selection