Advances in Automatic Text Summarization

By E. Liddy. Information retrieval (Boston), 2001

639 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/dd5641e798d97077ab49818448c64bfa494b29da

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Management Science and Operations Research

In Molecular Biology

Citations in the corpus

Automatic Text Summarization: Past, Present and Future (2013, background): “However, because of the importance of the research effort in automatic summarization, a series of proposals have been made to partially or fully automate the evaluation [73, 38].”
Automatic summarization of English broadcast news speech (2002, background): “Techniques of automatically summarizing written text have been actively investigated in the field of natural language processing [5].”
The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey (2021, neutral): “It aims at condensing a piece of text to a shorter version that contains the main information from the original document [Mani and Maybury, 1999; Nallapati et al., 2016].”
Multi-document multilingual summarization corpus preparation, Part 1: Arabic, English, Greek, Chinese, Romanian (2013, background): “The students were given no specific recommendations regarding the type of summary they should produce, e.g. an abstract versus an extract (Mani and Maybury, 1999), but they were specifically instructed to understand the main aspects of summarization.”
Summarization beyond sentence extraction: A probabilistic approach to sentence compression (2002, background): “Most research in automatic summarization has focused on extraction, i.e., on identifying the most important clauses/sentences/paragraphs in texts (see [23] for a representative collection of papers).”
Automatically Generating Wikipedia Articles: A Structure-Aware Approach (2009, methodology): “In text-to-text generation, such templates for information organization are not available; sentences are selected based on their salience properties (Mani and Maybury, 1999).”
A Reinforcement Learning Approach for Adaptive Single- and Multi-Document Summarization (2015, methodology): “As reviewing both in detail is beyond the scope of this article, we would like to point the interested reader to works by Nenkova and McKeown (2011), Mani and Maybury (1999) and Mani (2001) inter alia for an overview of the major developments in automatic summarization.”
Generic summarization and keyphrase extraction using mutual reinforcement principle and sentence clustering (2002, background): “To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior speci.c permission and/or a fee.”
Text Generation with Exemplar-based Adaptive Decoding (2019, neutral): “, text summarization (Mani, 1999), machine translation (Koehn, 2009), and data-to-text generation (Kukich, 1983; McKeown, 1992; Reiter and Dale, 1997).”
A Brief Survey of Text Mining (2005, neutral): “For an overview see [MM99] and [RHM02].”
Graph-Based Keyword Extraction for Single-Document Summarization (2008, background): “Some of the most successful approaches to ex-tractive summarization utilize supervised learning algorithms that are trained on collections of ”ground truth” summaries built for a relatively large number of documents (Mani and Maybury, 1999).”
BLEURT: Learning Robust Metrics for Text Generation (2020, background): “…paradigm (Sutskever et al., 2014; Bahdanau et al., 2015) which can tackle a wide array of tasks including translation (Koehn, 2009), summarization (Mani, 1999; Chopra et al., 2016), structured-data-to-text generation (McKeown, 1992; Kukich, 1983; Wiseman et al., 2017) dialog (Smith and Hipp,…”
Text summarization using unsupervised deep learning (2017, background): “In many research studies extractive summarization is equally known as sentence ranking (Edmundson, 1969; Mani and Maybury, 1999).”
DUC in context (2007, background): “Focus can be reflected in all three classes of factors involved in summarization (Sparck-Jones, 1998, 2001): input source, intended purpose, and the form of the output summary.”
Sticking to the Facts: Confident Decoding for Faithful Data-to-Text Generation (2019, background): “It is the essence of many natural language processing problems, such as text summarization (Mani, 1999), where x is a long document and y is a more concise version, machine translation (Koehn, 2009), where x and y represent equivalent text in different languages, and data-to-text generation…”
Automatic summarising: The state of the art (2007, background): “It must be emphasised that this review is not intended as a tutorial, and has somewhat different goals from such valuable earlier publications as Mani and Maybury (1999) and Mani (2001).”
HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models (2019, background): “Common generative modeling tasks include realistic image generation [18], machine translation [1], image captioning [57], and abstract summarization [39], among others.”