Text Summarization Techniques: A Brief Survey

By Mehdi Allahyari, Seyedamin Pouriyeh, Mehdi Assefi, S. Safaei, Elizabeth D. Trippe, Juan B. Gutiérrez, Krys J. Kochut. International Journal of Advanced Computer Science and Applications, 2017

558 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/12a50024da4b1ad71ddab2fb68785dc56c2e540f

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In Artificial Intelligence

Citations in the corpus

Abstractive Summarization of Reddit Posts with Multi-level Memory Networks (2018, background): “Abstractive summarization methods have been under intensive study, yet they often suffer from inferior performance compared to extractive meth-ods (Allahyari et al., 2017; Nallapati et al., 2017; See et al., 2017).”
Training language GANs from Scratch (2019, background): “Unsupervised word level text generation is a stepping stone for a plethora of applications, from dialogue generation to machine translation and summarization [1, 2, 3, 4].”
Global Voices: Crossing Borders in Automatic News Summarization (2019, background): “Despite the fast development of automatic summarization (Allahyari et al., 2017; Dong, 2018; Gambhir and Gupta, 2017), present technology mostly focuses on monolingual summarization.”
SUM-QE: a BERT-based Summary Quality Estimation Model (2019, methodology): “ROUGE is the most commonly used evaluation metric (Nenkova and McKeown, 2012; Allahyari et al., 2017; Gambhir and Gupta, 2017).”
Extractive Summarization of Long Documents by Combining Global and Local Context (2019, background): “This is a very challenging task, because it arguably requires an in-depth understanding of the source document, and current automatic solutions are still far from human performance (Allahyari et al., 2017).”
Abstract Text Summarization: A Low Resource Challenge (2019, unknown): “The abstract text summarization approach aims to shorten the long text into a humanreadable form that contains the most important fact from the original text (Allahyari et al., 2017; Kryściński et al., 2018).”
Cooperative Generator-Discriminator Networks for Abstractive Summarization with Narrative Flow (2019, result): “Our study contrasts with most previous work that focused on either ex-tractive document-level summarization (Nenkova and McKeown, 2012; Allahyari et al., 2017) or abstractive sentence-level summarization (Rush et al., 2015; Grusky et al., 2019; Narayan et al., 2018a), where maintaining a good…”
Speed Reading: Learning to Read ForBackward via Shuttle (2018, background): “…2014; Le and Mikolov, 2014a), cloze (Srinivasan et al., 2018), machine translation (Bahdanau et al., 2015), dialogue modeling (Mei et al., 2017), document summarization (Allahyari et al., 2017), automatic knowledge extraction (Durme and Schubert, 2008), and question answering (Chen et al., 2017).”
Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models (2025, unknown): “Summarization is defined as the process of creating shorter, coherent texts that accurately reflect the original content of a document [2].”