Applications of Automatic Text Summarization

About this research topic

The aim of Automatic Text Summarization (ATS) is to create a condensed version of a document that retains the most relevant topics and material.
ATS is not a modern area of research, but it has gotten a lot of coverage from the science community in recent years.
Modern neural network-based methods capable of producing very impressive outcomes, on the one hand, and the availability of large-scale datasets consisting of hundreds of thousands of document-summary pairs, on the other, have reignited interest. Furthermore, the ability to manage heterogeneous texts ranging from user-generated material derived from the internet to highly detailed documentation, such as technical/scientific articles, opens up new problems in this field of science. As a result, ATS is an important method not only for minimizing information material but also for assessing information validity and the appropriateness of answers in a given application context.

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This Research Focus aims to provide an overview of current research in the field of Natural Language Processing (NLP) and, in particular, ATS, in order to accelerate knowledge diffusion and allow the creation of new methods, datasets, and services that meet the needs of research and industry.
To that end, the Research Topic encourages an interdisciplinary approach, with the aim of bringing together scholars with diverse backgrounds in a variety of disciplines — machine learning, natural language, cognitive science, and psychology — to explore cutting-edge work as well as potential directions in this promising area of ATS for multiple sources of data.

This topic’s priorities include (but are not limited to) discussing the following issues:

  • summarization (abstractive and extractive)
  • topic-based/query-based summarization, supervised/unsupervised
  • text summarization (single and multi-document)
  • a variety of text genres (News, tweets, product reviews, meeting conversations, forums, lectures, student feedback, emails, medical records, books, research articles, etc)
  • summarization of different languages and through multiple languages
  • the development of new datasets and annotations, especially for languages other than English

Keywords: Automatic Text Summarization, Natural Language Processing, Long Documents, Human-like Summarization, Linguistics Constraints, summary generator, summarizer, summary tool, summarizer tool, article summarizer, pdf summarizer, free summarizer, summarizing tool, summarize text, summarize pdf

 

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