Automatic Summarization

By Inderjeet Mani, Mark T. Maybury. Annual Meeting of the Association for Computational Linguistics, 2001

699 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a181b1990b7d1e34a96654f8b8caed075ccd8c9d

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In Artificial Intelligence

Citations in the corpus

Pragmatically Informative Text Generation (2019, methodology): “tion (Nenkova and McKeown, 2011); recent neural models have used large datasets (e.”
Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization (2008, neutral): “Multi-document summarization is the process of generating a generic or topic-focused summary by reducing documents in size while retaining the main characteristics of the original documents [21, 27].”
Benchmarking Large Language Models for News Summarization (2023, background): “…of producing a concise paragraph that captures the main points of a news article and has been a core problem within the ﬁeld of automatic summarization (Radev et al., 2002; Rush et al., 2015; Nal-1 llm_summarization lapati et al., 2016; See et al., 2017; Chen and Bansal, 2018; Dong et al., 2019).”
Explorations in Automatic Book Summarization (2007, neutral): “The general belief in the text summarization literature (Edmunson, 1969; Mani, 2001) is that the position of sentences in a text represents one of the most important sources of information for a summarization system.”
Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity Model (2017, background): “It is widely applicable to many NLP tasks including summarization (Wong et al., 2008; Nenkova et al., 2011), generation and question answering (Vo et al., 2015), paraphrase detection (Fernando and Stevenson, 2008), and machine translation (Corley and Mihalcea, 2005).”