Multi-Sentence Compression: Finding Shortest Paths in Word Graphs

By Katja Filippova. 2010

237 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xipeng Qiu
2. Xiaojun Wan
3. Li Deng
4. Shay B. Cohen
5. Dong Yu
6. Zhiyuan Liu
7. Claire Gardent
8. Anastasia Shimorina

Citations in the corpus

• Abstractive Summarization of Spoken and Written Conversations Based on Phrasal Queries (2014, methodology): “the method recently proposed by (Mehdad et al., 2013a; Filippova, 2010) with some optimizations.”
• A New Annotated Portuguese/Spanish Corpus for the Multi-Sentence Compression Task (2018, methodology): “In the same way as (Filippova, 2010; Boudin and Morin, 2013), the native speakers evaluated the grammaticality in a 3-point scale: 0 point for an ungram-matical compression, 1 point for compression with minor mistakes; and 2 points for a correct compression.”
• Recent automatic text summarization techniques: a survey (2016, methodology): “A word graph is used to represent related sentences such that summaries form the paths in the graph (Filippova 2010).”
• Cross-language document summarization via extraction and ranking of multiple summaries (2019, background): “We further leverage takahe4 [6] for multiple English sentence compression.”
• Joint Parsing and Generation for Abstractive Summarization (2019, background): “…of recent studies 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…”
• Recent advances in document summarization (2017, background): “For instance, one can cluster sentences, build word graphs and generate (shortest) paths from each cluster to produce candidates for making up a summary [6,51].”
• Main Core Retention on Graph-of-Words for Single-Document Keyword Extraction (2015, methodology): “Hence, we recommend the use of undirected edges for ease of implementation but other techniques that would try to extract paths from the graph-of-words for instance might need the edge direction to follow the natural flow of the text like for multi-sentence compression [8].”
• Split and Rephrase (2017, background): “Sentence fusion consists of combining two or more sentences with over-lapping information content, preserving common information and deleting irrelevant details (McK-eown et al., 2010; Filippova, 2010; Thadani and McKeown, 2013).”
• HEADS: Headline Generation as Sequence Prediction Using an Abstract Feature-Rich Space (2015, methodology): “One of the most recent and competitive approaches for multi-sentence compression is described by Filippova (2010).”
• Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques (2020, background): “While early works focused on simply extracting important content from a document (Erkan and Radev, 2004; Wong et al., 2008), later approaches attempted to paraphrase the content into new sentences (abstractive summarization) (Filippova, 2010; Berg-Kirkpatrick et al., 2011; Wang and Cardie, 2013).”
• Unsupervised Sentence Enhancement for Automatic Summarization (2014, background): “A relatively large body of work exists in sentence compression (Knight and Marcu, 2000; McDonald, 2006; Galley and McKeown, 2007; Cohn and Lapata, 2008; Clarke and Lapata, 2008, inter alia), and sentence fusion (Barzilay and McKeown, 2005; Marsi and Krahmer, 2005; Filippova and Strube, 2008; Filippo”