Automatic text summarization: A comprehensive survey

By Wafaa S. El-Kassas, Cherif R. Salama, A. Rafea, H. K. Mohamed. Expert systems with applications, 2021

810 citations recorded by the source corpus.

Abstract Automatic Text Summarization (ATS) is becoming much more important because of the huge amount of textual content that grows exponentially on the Internet and the various archives of news articles, scientific papers, legal documents, etc. Manual text summarization consumes a lot of time, effort, cost, and even becomes impractical with the gigantic amount of textual content. Researchers have been trying to improve ATS techniques since the 1950s. ATS approaches are either extractive, abstractive, or hybrid. The extractive approach selects the most important sentences in the input document(s) then concatenates them to form the summary. The abstractive approach represents the input document(s) in an intermediate representation then generates the summary with sentences that are different than the original sentences. The hybrid approach combines both the extractive and abstractive approaches. Despite all the proposed methods, the generated summaries are still far away from the human-generated summaries. Most researches focus on the extractive approach. It is required to focus more on the abstractive and hybrid approaches. This research provides a comprehensive survey for the researchers by presenting the different aspects of ATS: approaches, methods, building blocks, techniques, datasets, evaluation methods, and future research directions.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/cbb8faf1051d858b4b0426742fdbbd0f104833ea

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In Artificial Intelligence

Citations in the corpus

Biases in Large Language Models: Origins, Inventory, and Discussion (2023, background): “Translation [24], Question Answering [94, 128], Information Retrieval [52, 130], Text Summarization [47, 49], Word Sense Disambiguation [6, 7, 14, 34, 86], Entity Linking [8, 25, 112], Semantic Role Labeling [18, 32, 33, 35, 105], Semantic Parsing [13, 85], and Natural Language Inference [90, 129], ”
Multi-document Summarization via Deep Learning Techniques: A Survey (2020, methodology): “Further research could also be based on techniques adopted in non-deep-learning-based MDS as reviewed in [39, 46, 142].”
Pre-Trained Language Models for Text Generation: A Survey (2022, background): “utterances in a conversation [224], in machine translation to translate a text from one language into another [31]; and in text summarization to generate an abridged summary of the source text [40].”