Discourse Constraints for Document Compression

By James Clarke, Mirella Lapata. 2010

91 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing

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 Natural Language Processing

Citations in the corpus

Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization (2018, methodology): “For our second experiment we used a question-answering (QA) paradigm (Clarke and Lapata, 2010; Narayan et al., 2018b) to assess the degree to which the models retain key information from the document.”
Text Summarization with Pretrained Encoders (2019, positive): “We report experiments following a questionanswering (QA) paradigm (Clarke and Lapata, 2010; Narayan et al., 2018b) which quantifies the degree to which summarization models retain key information from the document.”
Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints (2016, background): “, 2014), but one of the most pressing phenomena to deal with is pronoun anaphora (Clarke and Lapata, 2010).”
Ranking Sentences for Extractive Summarization with Reinforcement Learning (2018, methodology): “We used the same scoring mechanism from Clarke and Lapata (2010), i.e., a correct answer was marked with a score of one, partially correct answers with a score of 0.5, and zero otherwise.”
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).”
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation (2017, background): “…new texts from existing (usually humanwritten) text include: • fusion and summarization of related sentences or texts to make them more concise (e.g., Clarke & Lapata, 2010); • simplification of complex texts, for example to make them more accessible for low-literacy readers (e.g., Siddharthan,…”
FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization (2020, background): “Later, QA is incorporated in human evaluation where one person writes questions and another person answers them based on the summary (Clarke and Lapata, 2010; Liu and Lapata, 2019).”
QuestEval: Summarization Asks for Fact-based Evaluation (2021, methodology): “QA-Based Metrics QA-based approaches for summary evaluation were proposed a decade ago by Clarke and Lapata (2010) for human evaluation.”
Hierarchical Transformers for Multi-Document Summarization (2019, methodology): “We adopted the same scoring mechanism used in Clarke and Lapata (2010), i.e., correct answers are marked with 1, partially correct ones with 0.5, and 0 otherwise.”
Generating Summaries with Topic Templates and Structured Convolutional Decoders (2019, methodology): “We fol-Model lowed a question-answering (QA) scheme as proposed in Clarke and Lapata (2010).”
Movie Script Summarization as Graph-based Scene Extraction (2015, methodology): “We adopted a question-answering (Q&A) evaluation paradigm which has been used previously to evaluate summaries and document compression (Morris et al., 1992; Mani et al., 2002; Clarke and Lapata, 2010).”
BiSET: Bi-directional Selective Encoding with Template for Abstractive Summarization (2019, background): “…in this problem mainly focus on statistical or linguistic-rule-based methods, including those based on extractive and compression (Jing and McKeown, 2000; Knight and Marcu, 2002; Clarke and Lapata, 2010), templates (Zhou and Hovy, 2004) and statistical machine translation (Banko et al., 2000).”
SAFEval: Summarization Asks for Fact-based Evaluation (2021, methodology): “QA-Based Metrics QA-based approaches for summary evaluation were proposed a decade ago by Clarke and Lapata (2010) for human evaluation.”
Knowledge-Based Question Answering as Machine Translation (2014, background): “Recent works (Clarke and Lapata, 2010; Liang et al., 2013) have alleviated such issues using question-answer pairs as weak supervision, but still with the short-coming of using limited lexical triggers to link NL phrases to predicates.”