Unsupervised Sentence Enhancement for Automatic Summarization

By Jackie Chi Kit Cheung, Gerald Penn. 2014

41 citations recorded by the source corpus.

We present sentence enhancement as a novel technique for text-to-text generation in abstractive summarization. Compared to extraction or previous approaches to sentence fusion, sentence enhancement increases the range of possible summary sentences by allowing the combination of dependency subtrees from any sentence from the source text. Our experiments indicate that our approach yields summary sentences that are competitive with a sentence fusion baseline in terms of content quality, but better in terms of grammaticality, and that the benefit of sentence enhancement relies crucially on an event coreference resolution algorithm using distributional semantics. We also consider how text-to-text generation approaches to summarization can be extended beyond the source text by examining how human summary writers incorporate source-text-external elements into their summary sentences.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Get To The Point: Summarization with Pointer-Generator Networks (2017, methodology): “Prior to modern neural methods, abstractive summarization received less attention than extractive summarization, but Jing (2000) explored cutting unimportant parts of sentences to create summaries, and Cheung and Penn (2014) explore sentence fusion using dependency trees.”
Multi-Reward Reinforced Summarization with Saliency and Entailment (2018, background): “Recent focus has shifted towards abstractive, rewriting-based summarization based on parse trees (Cheung and Penn, 2014; Wang et al., 2016), Abstract Meaning Represen-tations (Liu et al., 2015; Dohare and Karnick, 2017), and neural network models with pointer-copy mechanism and coverage (Rush et…”
Abstractive Document Summarization with a Graph-Based Attentional Neural Model (2017, methodology): “Previous research is mostly restricted in one or a few of the subproblems or specific domains (Woodsend and Lapata, 2012; Thadani and McKeown, 2013; Cheung and Penn, 2014; Pighin et al., 2014; Sun et al., 2015).”
Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation (2018, background): “, 2014), syntactic parse trees (Cheung and Penn, 2014; Wang et al., 2013), and Abstract Meaning Representations (AMR) (Liu et al.”
Recent advances in document summarization (2017, background): “2) caseframes [29], grammar-based [195], recombining units [30, 121, 13], extraction templates [59, 166, 154] End-to-end (Section 2.”
Toward Abstractive Summarization Using Semantic Representations (2018, background): “According to Dang and Owczarzak (2008), the majority of competitive summarization systems are extractive, selecting representative sentences from input documents and concatenating them to form a summary.”