New Methods in Automatic Extracting

By H. P. Edmundson. 1969

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In Natural Language Processing

Citations in the corpus

Towards Coherent and Cohesive Long-form Text Generation (2018, background): “…such as Rhetorical Structure Theory (Mann and Thompson, 1988), and other forms of coherence and cohesion relations and their quantiﬁ-cation (Edmundson, 1969; Halliday and Hasan, 1976; Hobbs, 1985; McKeown, 1985; Cohen and Levesque, 1985; Hovy, 1988; Liddy, 1991; Hovy, 1991; Mani et al.,…”
LexRank: Graph-based Lexical Centrality as Salience in Text Summarization (2004, background): “Early research on extractive summarization is based on simple heuristic features of the sentences such as their position in the text, the overall frequency of the words they contain, or some key phrases indicating the importance of the sentences (Baxendale, 1958; Edmundson, 1969; Luhn, 1958).”
Hedge Trimmer: A Parse-and-Trim Approach to Headline Generation (2003, background): “, sentence extraction (Edmundson, 1969; Johnson et al, 1993; Kupiec et al., 1995; Mann et al., 1992; Teufel and Moens, 1997; Zechner, 1995), processing of structured templates (Paice and Jones, 1993), sentence compression (Hori et al.”
Abstractive Document Summarization with a Graph-Based Attentional Neural Model (2017, background): “Notable early works include (Edmundson, 1969; Carbonell and Goldstein, 1998; McDonald, 2007).”
Using Cohesion and Coherence Models for Text Summarization (1998, result): “This is in keeping with the assumption underlying much summarization research that location and linear order of text items are important (e.g., ( Edmundson 1969 ), (Kupiec, Pedersen, and Chen 1995), (Paice and Jones 1993)) in determining what’s salient.”
Learning Algorithms for Keyphrase Extraction (2000, background): “…(Turney, 1997, 1999 Several papers explore the task of producing a summary of a document by extracting key sentences from the document (Luhn, 1958; Edmundson, 1969; Marsh et al., 1984; Paice, 1990; Paice and Jones, 1993; Johnson et al., 1993; Salton et al., 1994; Kupiec et al., 1995; Brandow et…”
Enhancing Single-Document Summarization by Combining RankNet and Third-Party Sources (2007, background): “sentence position, terms common with the title, appearance of keyword terms, and other cue phrases is known as the Edmundsonian Paradigm (Edmundson, 1969; Alfonesca and Rodriguez, 2003; Mani, 2001).”
A Survey on Automatic Text Summarization (2007, methodology): “F k ) = k i=1 P (F i | s ∈ S) · P (s ∈ S) k i=1 P (F i ) (1) The features were compliant to (Edmundson, 1969), but additionally included the sentence length and the presence of uppercase words.”
A Survey of Text Summarization Extractive Techniques (2010, methodology): “Paragraph Feature: this is basically equivalent to Location Method feature in [8] 4.”
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).”
Text Summarization Techniques: A Brief Survey (2017, methodology): “Edmundson et al. [23] described a paradigm based on key phrases which in addition to standard frequency depending weights, used the following three methods to determine the sentence weight: (1) Cue Method:Therelevance ofa sentence is calculatedbased on the presence or absence of certain cue words…”
A Survey of Text Summarization Techniques (2012, background): “Edmundson’s early work [23] set the direction for later investigation of applying machine learning techniques for summarization [43].”
A New Approach to Improving Multilingual Summarization Using a Genetic Algorithm (2010, methodology): “…-based (Luhn, 1958; Edmund-son, 1969; Neto et al., 2000; Steinberger and Jezek, 2004; Kallel et al., 2004; Vanderwende et al., 2007), title -based (Edmundson, 1969; Teufel and Moens, 1997), position -based (Baxendale, 1958; Edmundson, 1969; Lin and Hovy, 1997; Satoshi et al., 2001) and length…”
Explorations in Automatic Book Summarization (2007, background): “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.”
Automatic Text Summarization Using a Machine Learning Approach (2002, methodology): “, and has presented good results in several research projects [5], [7], [8], [11], [23].”
Temporal summaries of new topics (2001, background): “TDT investigation has been carried out over ﬁve years by about a dozen academic and industrial research institutions, and explored in the context of four “coopera-tively competitive” evaluations sponsored by the U.S. government [1, 7, 28, 29].”
Learning to Extract Keyphrases from Text (2002, background): “Several papers explore the task of producing a summary of a document by extracting key sentences from the document (Luhn, 1958; Edmundson, 1969; Marsh et al., 1984; Paice, 1990; Paice and Jones, 1993; Johnson et al., 1993; Salton et al., 1994; Kupiec et al., 1995; Brandow et al., 1995; Jang and Myae”
SUMMAC: a text summarization evaluation (2002, background): “…approaches select material from the source based on weights for features such as (1) position in some structural representation of the document (Edmundson 1969; Kupiec, Pedersen and Chen 1995; Hovy and Lin 1999), (2) presence of cue phrases (Edmundson 1969; Pollock and Zamora 1975) like \in…”
Automatic Text Summarization: Past, Present and Future (2013, methodology): “The position of sentences in text [12, 30] is also deemed indicative of sentence relevance.”