Automatic Condensation of Electronic Publications by Sentence Selection

By Ronald Brandow, Karl Mitze, L. Rau. 1995

Subfields: Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

WikiLingua: A New Benchmark Dataset for Multilingual Abstractive Summarization (2020, background): “We note that, by design, the summaries do not incorporate any potential lead bias, which stands in contrast to single document news summarization, where position is an influential signal (Brandow et al., 1995).”
LexRank: Graph-based Lexical Centrality as Salience in Text Summarization (2004, methodology): “This is tantamount to producing lead-based summaries, which is a widely used and very challenging baseline in the text summarization community (Brandow, Mitze, & Rau, 1995).”
Content Selection in Deep Learning Models of Summarization (2018, background): “Document Shuffling Sentence position is a well known and powerful feature for news summarization (Hong and Nenkova, 2014), owing to the intentional lead bias in the news article writing10; it also explains the difficulty in beating the lead baseline for single-document summarization (Nenkova, 2005; ”
Automatic Text Summarization Using a Machine Learning Approach (2002, unknown): “Although very simple, this procedure provides a relatively strong baseline for the performance of any text-summarization method [2].”
A Survey of Text Summarization Extractive Techniques (2010, methodology): “The ANES text extraction system [10] in 1995 is a system that performs automatic, domain-independent condensation of news data.”
SUMMAC: a text summarization evaluation (2002, methodology): “The extrinsic evaluation of Brandow et al .”
From Extractive to Abstractive Summarization: A Journey (2019, background): “Unfortunately, most of the initial works were either not reproducible due to lack of standard evaluation corpora [5], [38] or worse, they were not evaluated at all.”
LexPageRank: Prestige in Multi-Document Text Summarization (2004, background): “This is tan-tamount to producing lead-based summaries, which is a widely used and very challenging baseline in the text summarization community (Brandow et al., 1995).”
Articles Summarizing Scientific Articles: Experiments with Relevance and Rhetorical Status (2002, background): “…to the summarization of news articles make use of the typical journalistic writing style, for example, the fact that the most newsworthy information comes ﬁrst; as a result, the ﬁrst few sentences of a news article are good candidates for a summary (Brandow, Mitze, and Rau 1995; Lin and Hovy 1997).”
The TIPSTER SUMMAC Text Summarization Evaluation (1999, background): “Instead, the systems more often relied on inclusion of the first sentence of the source -a useful strategy for newswire (Brandow et al. 1994): the generic (categorization) summaries had a higher percentage of selections of first sentences from the source than the adhoc summaries (35% of S1 and 41%…”
Evaluation of Text Summarization in a Cross-lingual Information Retrieval Framework (2011, methodology): “The main evaluation metrics used in intrinsic evaluations are precision, recall and F-score (Firmin and Chrzanowski, 1999), which have been extensively used in the past to evaluate text summarization systems (Edmundson, 1969; Brandow et al., 1995; Marcu, 1997; Barzilay and Elhadad, 1997).”
Introduction to the Special Issue on Summarization (2002, background): “Form metrics focus on grammaticality, overall text coherence, and organization and are usually measured on a point scale (Brandow, Mitze, and Rau 1995).”
Advantages of query biased summaries in information retrieval (1998, result): “This conclusion seemed to be in agreement with (Brandow et al. 1995).”
LexRank: Graph-based Centrality as Salience in Text Summarization (2004, methodology): “This is tantamount to producing lead-based summaries, which is a widely used and very challenging baseline in the text summarization community ( Brandow, Mitze, & Rau, 1995 ).”
Learning to Extract Keyphrases from Text (2002, methodology): “…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 Myaeng, 1997).”
Learning Algorithms for Keyphrase Extraction (2000, methodology): “The extracted sentences often lack cohesion because anaphoric references are not resolved (Johnson et al., 1993; Brandow et al., 1995).”
Temporal summaries of new topics (2001, background): “The extremes are after every sentence, and when all sentences have been seen.”
Automatic summarising: The state of the art (2007, methodology): “Edmundson (1969)’s and Brandow et al. (1995)’s requirement for summary acceptability against source was very weak.”