Introduction to the Special Issue on Summarization

By Dragomir R. Radev, Eduard H. Hovy, K. McKeown. International Conference on Computational Logic, 2002

597 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f4186ca557b987010cf5c7fe3674d5d380f116df

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In Artificial Intelligence

Citations in the corpus

Automatic labeling of multinomial topic models (2007, unknown): “, [21]).”
Opinion Mining and Sentiment Analysis (2008, background): “summarizers as well [248]; this has been one of the motivations behind work on paraphrase recognition [29, 30, 231] and textual entailment [28].”
Opinosis: A Graph Based Approach to Abstractive Summarization of Highly Redundant Opinions (2010, background): “Going strictly by the deﬁnition of true abstraction (Radev et al., 2002), our problem formulation is still more extractive than abstractive because the generated summary can only contain words that occur in the text to be summarized; our problem deﬁnition may be regarded as a word-level (ﬁner…”
Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization (2008, background): “Another issue is identifying important di(cid:11)erence between documents and covering the informative content as much as possible [25].”
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).”
Intrinsic Evaluation of Summarization Datasets (2020, methodology): “We are guided by pioneering work (Luhn, 1958; Ed-mundson, 1969; Mani, 1999) that deﬁned core properties of summarization systems and inﬂuential sub-5 sequent work (Radev et al., 2002; Nenkova, 2006; Nenkova and McKeown, 2012; Peyrard, 2019a) that reﬁned and extended these properties.”
A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques (2017, background): “Text Summarization:Many text mining applications need to summarize the text documents in order to get a concise overview of a large document or a collection of documents on a topic [67, 115].”
A Survey on Automatic Text Summarization (2007, background): “…extraction is the procedure of identifying important sections of the text and producing them verbatim; abstraction aims to produce important material in a new way; fusion combines extracted parts coherently; and compression aims to throw out unimportant sections of the text (Radev et al., 2002).”
Multi-document Summarization via Deep Learning Techniques: A Survey (2020, background): “rt a text or a collection of texts within the same topic into a concise summary that contains key semantic information. The length of summaries is usually significantly less than the original text(s) [104]. The research on automatic text summarization has been attractive in the field of natural lang”
Text Summarization Techniques: A Brief Survey (2017, background): “According to Radef et al. [53] a summary is deﬁned as “a text that is produced from one or more texts, that conveys important information in the original text(s), and that is no longer than half of the original text(s) and usually, signiﬁcantly less than that”.”
A Brief Survey of Text Mining (2005, unknown): “For an overview see [MM99] and [RHM02].”
DialogSum: A Real-Life Scenario Dialogue Summarization Dataset (2021, background): “summary of a given set of documents (Radev et al., 2002).”