Neural Network-Based Abstract Generation for Opinions and Arguments

By Wang Ling. 2016

149 citations recorded by the source corpus.

We study the problem of generating abstractive summaries for opinionated text. We propose an attention-based neural network model that is able to absorb information from multiple text units to construct informative, concise, and fluent summaries. An importance-based sampling method is designed to allow the encoder to integrate information from an important subset of input. Automatic evaluation indicates that our system outperforms state-of-the-art abstractive and extractive summarization systems on two newly collected datasets of movie reviews and arguments. Our system summaries are also rated as more informative and grammatical in human evaluation.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Finetuned Language Models Are Zero-Shot Learners (2021, background): “Opinion Abstracts - Rotten Tomatoes (Wang & Ling, 2016) 10.”
Abstractive Summarization of Reddit Posts with Multi-level Memory Networks (2018, methodology): “The Idebate dataset is collected from short arguments of debates on controversial topics, and thus the text is rather formal.”
AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization (2021, background): “, 2019), email (Zhang and Tetreault, 2019), movie review (Wang and Ling, 2016), debate (Wang and Ling, 2016), social media (Kim et al.”
Aspect-Controlled Neural Argument Generation (2020, background): “A rather new ﬁeld for these models is the task of producing text with ar-gumentative content (Wang and Ling, 2016).”
Unsupervised Neural Single-Document Summarization of Reviews via Learning Latent Discourse Structure and its Ranking (2019, methodology): “Several previous studies have addressed abstrac-tive summarization for product reviews (Carenini et al., 2013; Di Fabbrizio et al., 2014; Bing et al., 2015; Yu et al., 2016); however, their output summaries are not guaranteed to be grammatical (Wang and Ling, 2016).”
Graph-based Neural Multi-Document Summarization (2017, background): “Neural networks have recently been popular for text summarization (K˚ageb¨ack et al., 2014; Rush et al., 2015; Yin and Pei, 2015; Cao et al., 2016; Wang and Ling, 2016; Cheng and Lapata, 2016; Nallapati et al., 2016, 2017; See et al., 2017).”
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.”
Multi-document Summarization via Deep Learning Techniques: A Survey (2020, background): “iews are fetched from different sources. For each cluster, five professional written golden summaries are provided for the model training and evaluation. RottenTomatoesDataset.Rotten tomatoes dataset [134] consists of the collected reviews of 3,731 movies from rotten tomato website12. Among them, th”
SummScreen: A Dataset for Abstractive Screenplay Summarization (2021, background): “, 2019), opinionated text (RottenTomatoes; Wang and Ling, 2016), patents (Sharma et al.”