The Challenges of Automatic Summarization

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How researchers see this paper

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Generating Sentences by Editing Prototypes (2017, background): “…to generate sentences is a core component of many NLP systems, including machine translation (Kalchbrenner and Blunsom, 2013; Koehn et al., 2007), summarization (Hahn and Mani, 2000; Nallapati et al., 2016), speech recognition (Ju-rafsky and Martin, 2000), and dialogue (Ritter et al., 2011).”
Digital technologies and artificial intelligence’s present and foreseeable impact on lawyering, judging, policing and law enforcement (2015, methodology): “Hahn and Mani 2000). Another book on the subject is Moens (2000), whereas Moens (2001) is on legal text retrieval.”
A new sentence similarity measure and sentence based extractive technique for automatic text summarization (2009, background): “The technology of automatic document summarization is maturing and may provide a solution to the information overload problem (Hahn & Mani, 2000; Mani & Maybury, 1999).”
The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey (2021, neutral): “However, previous paraphrase-based generation methods are easy to produce influent sentences [Hahn and Mani, 2000].”
Beyond the hype: Big data concepts, methods, and analytics (2015, background): “As a result, abstractive systems tend to generate more coherent summaries than the extractive systems do (Hahn & Mani, 2000).”