Automatic Generation of Story Highlights

By Kristian Woodsend, Mirella Lapata. 2010

122 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension (2018, background): “In addition, news providers such as CNN and Daily Mail supplement their articles with a number of bullet points (Svore et al., 2007; Woodsend and Lapata, 2010; Hermann et al., 2015), which out-line the highlights of the news and hence form a supplemental source for generating passages.”
Teaching Machines to Read and Comprehend (2015, methodology): “Inspired by work in summarisation [10, 11], we create two machine reading corpora by exploiting online newspaper articles and their matching summaries.”
Neural Summarization by Extracting Sentences and Words (2016, methodology): “Until now such corpora have been limited to hundreds of examples (e.g., the DUC 2002 single document summarization corpus) and thus used mostly for testing (Woodsend and Lapata, 2010).”
Ranking Sentences for Extractive Summarization with Reinforcement Learning (2018, neutral): “, 2009), or integer-linear programming (Woodsend and Lapata, 2010; Berg-Kirkpatrick et al., 2011; Woodsend and Lapata, 2012; Almeida and Martins, 2013; Parveen et al., 2015).”
Overcoming the Lack of Parallel Data in Sentence Compression (2013, neutral): “Similarly, Woodsend & Lapata (2010) formulate an ILP problem to generate news story highlights using precomputed scores.”
Guiding Extractive Summarization with Question-Answering Rewards (2019, background): “stracts to derive labels for extraction units (Woodsend and Lapata, 2010).”
Neural Latent Extractive Document Summarization (2018, background): “Early attempts mostly leverage human-engineered features (Filatova and Hatzivassiloglou, 2004) coupled with binary classiﬁers ( Kupiec et al., 1995), hidden Markov models (Conroy and O’leary, 2001), graph based methods (Mihalcea, 2005), and inte-ger linear programming (Woodsend and Lapata, 2010).”
HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization (2019, methodology): “It is usually modeled as a sentence ranking problem by using the scores from classiﬁers (Kupiec et al., 1995), sequential labeling models (Conroy and O’leary, 2001) as well as integer linear programmers (Woodsend and Lapata, 2010).”
Jointly Learning to Extract and Compress (2011, background): “Both Martins and Smith (2009) and Woodsend and Lapata (2010) build models that jointly extract and compress, but learn scores for sentences (or phrases) using independent classiﬁers.”
Title Generation with Quasi-Synchronous Grammar (2010, background): “Unfortunately, extracts generated this way are often documents of low readability and text quality, and contain much redundant information.”
Collective Generation of Natural Image Descriptions (2012, unknown): “We employ Integer Linear Programming (ILP) as an optimization framework that has been used successfully in other generation tasks (e.g., Clarke and Lapata (2006), Mar-tins and Smith (2009), Woodsend and Lapata (2010)).”
Neural Extractive Summarization with Side Information (2017, methodology): “Cheng and Lapata (2016) annotated this dataset with the Woodsend and Lapata (2010) style gold annotation using a rule-based method judging each sentence for its semantic correspondence to the gold summary.”