Automatic summarising: The state of the art

By Karen Spärck Jones. Information Processing & Management, 2007

402 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/802b58b789338b2325d4d7361044809f3e2ca949

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In Artificial Intelligence

Citations in the corpus

A Supervised Approach to Extractive Summarisation of Scientific Papers (2017, background): “Keyphrase Score Authors such as Spärck Jones (2007) refer to the keyphrase score as a useful summarisation feature.”
Ranking Sentences for Extractive Summarization with Reinforcement Learning (2018, background): “der to identify the most important sentences in a document or set of documents (Kupiec et al., 1995; Mani, 2001; Radev et al., 2004; Filatova and Hatzivassiloglou, 2004; Nenkova et al., 2006; Spa¨rck Jones, 2007). A vast majority of these methods learn to score each sentence independently (Barzilay ”
News Summarization and Evaluation in the Era of GPT-3 (2022, background): “Jones (2007) discusses the need to align task formulations with actual applications scenarios (“purpose factors”).”
On the Use of Automated Text Summarization Techniques for Summarizing Source Code (2010, methodology): “To that end we propose adapting and using techniques from the field of automated text summarization [5], in order to automatically generate source code summaries.”
Recent automatic text summarization techniques: a survey (2016, background): “A large number of techniques and approaches have been developed in this field of research (Jones 2007).”
Text Summarization Techniques: A Brief Survey (2017, background): “arization methods and provide an overview of some of the most dominant approaches in this category. There are a number of papers thatprovideextensiveoverviewsoftextsummarizationtechniques and systems [37, 46, 58, 67]. 2 EXTRACTIVE SUMMARIZATION Asmentionedbefore,extractivesummarizationtechniquesprod”
A Hidden Markov Model to detect coded information islands in free text (2013, background): “Traceability recovery [6], impact analysis [9], bug report assignment [10], [11], code/text summarization [12], [13] are approaches that could benefit from methods that are able to discriminate noisy data and relevant information.”
Automatic Text Summarization: Past, Present and Future (2013, background): “For the interested reader, there are a number of papers and books that provide extensive overviews of text summarization systems and methods [36, 23, 33].”
Supporting program comprehension with source code summarization (2010, methodology): “The approach is inspired by automated summarization [ 9 ], which is the creation of a shortened version of a text by a computer program.”
Neural Extractive Summarization with Side Information (2017, background): “This paper addresses the task of single-document summarization and explores how we can further improve the sentence selection process for extractive summarization.”
Evaluation of a Cross-lingual Romanian-English Multi-document Summariser (2008, background): “More detailed information about these topics can be found in Mani (2001), Hovy (2003) or Sparck Jones (2007).”
Autofolding for Source Code Summarization (2014, background): “The task of natural language summarization has been studied extensively [29], mostly focusing on extractive summarization—the problem of extracting the most relevant text segments from documents.”
A new sentence similarity measure and sentence based extractive technique for automatic text summarization (2009, background): “The paper (Jones, 2007) reviews research on automatic summarizing over the last decade.”