Recent advances in document summarization

By Jin-ge Yao, Xiaojun Wan, Jianguo Xiao. 2017

161 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Learning to Extract Coherent Summary via Deep Reinforcement Learning (2018, background): “Coherence is what makes multiple sentences semantically, logically and syntactically coherent (Yao, Wan, and Xiao 2017).”
Abstractive Document Summarization with a Graph-Based Attentional Neural Model (2017, background): “Each of these techniques has large space to be improved (Yao et al., 2017).”
Entity Commonsense Representation for Neural Abstractive Summarization (2018, methodology): “Due to the limitations of extractive summarization on incoherent texts and unnatural methodology (Yao et al., 2017), the research trend has shifted towards abstractive summarization.”
Controlling Length in Abstractive Summarization Using a Convolutional Neural Network (2018, methodology): “We introduce the similarity metric here to complement the ROUGE scores because Yao et al. (2017a) showed that the standard ROUGE scores cannot capture semantic similarity beyond n-grams.”
Discourse-Aware Neural Extractive Text Summarization (2019, background): “Neural networks have achieved great success in the task of text summarization (Nenkova et al., 2011; Yao et al., 2017).”
From Local to Global: A Graph RAG Approach to Query-Focused Summarization (2024, background): “Instead, a more appropriate task framing is query-focused summarization (QFS, Dang, 2006), and in particular, query-focused abstractive summarization that generates natural language summaries and not just concatenated excerpts (Baumel et al., 2018; Laskar et al., 2020; Yao et al., 2017) .”