Opinosis: A Graph Based Approach to Abstractive Summarization of Highly Redundant Opinions

By Kavita A. Ganesan, Chengxiang Zhai, Jiawei Han. 2010

558 citations recorded by the source corpus.

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Citations in the corpus

A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification (2015, neutral): “contains approximately 100 sentences (Ganesan et al., 2010).”
Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation (2018, background): “…Kedzie et al., 2015), and moving more towards compressive and abstractive summarization based on graphs and concept maps (Giannakopoulos, 2009; Ganesan et al., 2010; Falke and Gurevych, 2017) and discourse trees (Gerani et al., 2014), syntactic parse trees (Cheung and Penn, 2014; Wang et al.,…”
Multi-Reward Reinforced Summarization with Saliency and Entailment (2018, methodology): “, 2015), with more focus on graph-based (Giannakopoulos, 2009; Ganesan et al., 2010) and discourse tree-based (Gerani et al.”
How well do you know your summarization datasets? (2021, unknown): “, 2018a), and many more (Wang and Ling, 2016; Koupaee and Wang, 2018; Kim et al., 2019; Ganesan et al., 2010) were collected by scraping a large collection of web-pages.”
Few-Shot Learning for Opinion Summarization (2020, background): “OPINOSIS (Ganesan et al., 2010) does not use any supervision and relies on POS tags and redundancies to generate short opinions.”
SummaRuNNer: A Recurrent Neural Network Based Sequence Model for Extractive Summarization of Documents (2016, methodology): “For example, (Kageback et al. 2014) employed the recursive autoencoder (Socher et al. 2011) to summarize documents, producing best performance on the Opinosis dataset (Ganesan, Zhai, and Han 2010).”
Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model (2019, background): “…summarization have been both extractive (Carbonell and Goldstein, 1998; Radev et al., 2000; Erkan and Radev, 2004; Mihalcea and Ta-rau, 2004; Haghighi and Vanderwende, 2009) as well as abstractive (McKeown and Radev, 1995; Radev and McKeown, 1998; Barzilay et al., 1999; Ganesan et al., 2010).”
Data Augmentation for Abstractive Query-Focused Multi-Document Summarization (2021, methodology): “Earlier abstractive MDS focused on template-and planner-based (McKeown and Radev 1995; Radev and McKeown 1998; Barzilay, McKeown, and Elhadad 1999) and graph-based methods (Ganesan, Zhai, and Han 2010).”
Unsupervised Neural Single-Document Summarization of Reviews via Learning Latent Discourse Structure and its Ranking (2019, methodology): “As an unsupervised word-level extractive approach, we employ Opinosis (Ganesan et al., 2010), which detects salient phrases in terms of their redundancy.”
Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs (2019, background): “2018) and graphs (Ganesan et al., 2010; Yasunaga et al., 2017).”
Why I like it: multi-task learning for recommendation and explanation (2018, background): “(iv) Opinosis: Opinosis [15] is a graph based abstractive summarization framework that is capable of eliminating redundant opinions.”
Aspect-Controllable Opinion Summarization (2021, background): “Earlier work on opinion summarization has focused on general summarization using extractive (Hu and Liu, 2006; Kim et al., 2011; Angelidis and Lap-ata, 2018b) or abstractive methods (Ganesan et al., 2010; Carenini et al., 2013; Fabbrizio et al., 2014).”
Sentiment analysis: A review and comparative analysis of web services (2015, background): “The final summary can be presented as a graphic or a text showing the main features/entities and quantifying the sentiment with regard to each one in some way, for example, aggregating intensities of sentiments or counting the number of positive or negative sentences [9,67,69,63,62,23,24,85].”
Recent advances in document summarization (2017, methodology): “For example, abstract-based approaches have been studied for product reviews where graph-based algorithms can be designed to merge reviews with similar textual content [55].”
Toward Abstractive Summarization Using Semantic Representations (2018, background): “ (Qian and Liu, 2013), among others. Previous work on abstractive summarization has explored user studies that compare extractive with NLG-based abstractive summarization (Carenini and Cheung, 2008). Ganesan et al. (2010) propose to construct summary sentences by repeatedly searching the highest sco”
Abstractive Summarization of Spoken and Written Conversations Based on Phrasal Queries (2014, background): “(Barzilay and McKeown, 2005; Liu and Liu, 2009; Ganesan et al., 2010; Murray et al., 2010)); however, most of them rely heavily on the sentence structure.”
From Extractive to Abstractive Summarization: A Journey (2019, methodology): “Graph based techniques similar to (Ganesan et al., 2010) and (Banerjee et al., 2015) have become very popular recently.”