Jointly Learning to Extract and Compress

By Taylor Berg-Kirkpatrick, D. Gillick, Daniel Klein. 2011

238 citations recorded by the source corpus.

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Citations in the corpus

Learning to Extract Coherent Summary via Deep Reinforcement Learning (2018, methodology): “Traditional methods mainly focus on scoring sentences using graph-based method (Erkan and Radev 2004), submodular functions (Lin and Bilmes 2011) or integer linear programming (Berg-Kirkpatrick, Gillick, and Klein 2011), which are coupled with handcrafted features.”
Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints (2016, methodology): “compressive summarization (Lin, 2003; Martins and Smith, 2009; Berg-Kirkpatrick et al., 2011; Woodsend and Lapata, 2012; Almeida and Martins, 2013), we wish to be able to compress sentences so we can pack more information into a summary.”
Ranking Sentences for Extractive Summarization with Reinforcement Learning (2018, methodology): “, 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).”
Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting (2018, methodology): “…used Hidden Markov Models and rule-based systems (Jing and McKeown, 2000), statistical models based on parse trees (Knight and Marcu, 2000), and integer linear programming based methods (Martins and Smith, 2009; Gillick and Favre, 2009; Clarke and Lapata, 2010; Berg-Kirkpatrick et al., 2011).”
Overcoming the Lack of Parallel Data in Sentence Compression (2013, background): “…a compression may differ lexically and structurally from the source sentence, to date most systems are ex-tractive and proceed by deleting words from the input (Knight & Marcu, 2000; Dorr et al., 2003; Turner & Charniak, 2005; Clarke & Lapata, 2008; Berg-Kirkpatrick et al., 2011, inter alia).”
Sentence Compression by Deletion with LSTMs (2015, background): “That is, one usually drops constituents or prunes dependency edges (Jing, 2000; Knight & Marcu, 2000; McDonald, 2006; Clarke & Lapata, 2008; Berg-Kirkpatrick et al., 2011; Filippova & Altun, 2013).”
Content Selection in Deep Learning Models of Summarization (2018, background): “Typically they involve learning n-gram feature weights in linear models along with other non-lexical word or structural features (Berg-Kirkpatrick et al., 2011; Sipos et al., 2012; Durrett et al., 2016).”
Multi-Task Learning for Sequence Tagging: An Empirical Study (2018, methodology): “…MWE (Constant and Nivre, 2016), or with semantic role labeling (SRL) (Shi et al., 2016); machine translation (TRANSLATE) with POS or DEP (Niehues and Cho, 2017; Eriguchi et al., 2017); sentence extraction and COM (Martins and Smith, 2009; Berg-Kirkpatrick et al., 2011; Almeida and Martins, 2013).”
The Role of Discourse Units in Near-Extractive Summarization (2016, methodology): “This setting is also explored in the summarization literature for techniques which combine extractive sentence selection with sentence compression (Clarke and Lapata, 2007; Berg-Kirkpatrick et al., 2011; Woodsend and Lapata, 2012; Almeida and Martins, 2013; Kikuchi et al., 2014).”
Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs (2019, methodology): “, 2008), extractive methods to select relevant sentences (Daumé III and Marcu, 2002; Gillick and Favre, 2009; Berg-Kirkpatrick et al., 2011; Di Fabbrizio et al., 2014; Bing et al., 2015; Cao et al., 2017) including maximal marginal relevance (Fabbri et al.”
Evidence Extraction for Machine Reading Comprehension with Deep Probabilistic Logic (2018, methodology): “2 Integer Linear Programming (ILP) Inspired by concept-based ILP models for summarization (Berg-Kirkpatrick et al., 2011; Boudin et al., 2015), we regard evidence sentence selection as a maximum coverage problem.”
UvA-DARE (Digital Academic Repository) A Dataset and Evaluation Metrics for Abstractive Compression of Sentences and Short Paragraphs (unknown): “Automated sentence compression condenses a sentence or paragraph to its most important content in order to enhance writing quality, meet document length constraints, and build more accurate document summarization systems (Berg-Kirkpatrick et al., 2011; Vanderwende et al., 2007).”
Annotating and Modeling Fine-grained Factuality in Summarization (2021, background): “Earlier work on abstraction (Barzilay et al., 1999; Carenini and Cheung, 2008) and compression (Knight and Marcu, 2000; Berg-Kirkpatrick et al., 2011; Woodsend and Lapata, 2012; Durrett et al., 2016) in summarization has typically focused evaluation on content selection and grammaticality, with…”
Evidence Sentence Extraction for Machine Reading Comprehension (2019, methodology): “Integer Linear Programming (ILP) Inspired by ILP models for summarization (Berg-Kirkpatrick et al., 2011; Boudin et al., 2015), we model evidence sentence selection as a maximum coverage problem and define the value of a selected sentence set as the sum of the weights for the unique words it contain”
Improving the Estimation of Word Importance for News Multi-Document Summarization (2014, methodology): “Most optimization approaches, however, use TF*IDF or word probability in the input as word weights (McDonald, 2007; Shen and Li, 2010; Berg-Kirkpatrick et al., 2011).”
Toward Abstractive Summarization Using Semantic Representations (2018, methodology): “ILPs and approximations have been used to encode compression and extraction (McDonald, 2007; Martins and Smith, 2009; Gillick and Favre, 2009; Berg-Kirkpatrick et al., 2011; Almeida and Martins, 2013; Li et al., 2014).”
Text Summarization Techniques: A Brief Survey (2017, background): “Existing abstractive summarizers often rely on an extractive preprocessing component to produce the abstract of the text [11, 33].”
Joint Parsing and Generation for Abstractive Summarization (2019, background): “…have explored constituency and dependency grammars (Daum´e III and Marcu 2002; Clarke and Lapata 2008; Martins and Smith 2009; Filippova 2010; Berg-Kirkpatrick, Gillick, and Klein 2011; Wang et al. 2013; Durrett, Berg-Kirkpatrick, and Klein 2016), rhetorical structure (Christensen et al.…”