BiSET: Bi-directional Selective Encoding with Template for Abstractive Summarization

Wang, Kai; Quan, Xiaojun; Wang, Rui

doi:10.18653/v1/p19-1207

Cited by 59 publications

(48 citation statements)

References 20 publications

(32 reference statements)

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“…In recent years, sequence-to-sequence (seq2seq) [61] based neural networks have been proved effective in generating a fluent sentence. The seq2seq model is originally proposed for machine translation and later adapted to various natural language generation tasks, such as text summarization [10,18,19,22,25,41,48,69,71] and dialogue generation [6,17,20,21,40,50,64,81,85,86]. Rush et al [53] apply the seq2seq mechanism with attention model to text summarization field.…”

Section: Text Generation Methodsmentioning

confidence: 99%

Product-Aware Answer Generation in E-Commerce Question-Answering

Gao

Zhang

Zhao

et al. 2019

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

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In e-commerce portals, generating answers for product-related questions has become a crucial task. In this paper, we propose the task of product-aware answer generation, which tends to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes. Unlike existing question-answering problems, answer generation in e-commerce confronts three main challenges: (1) Reviews are informal and noisy; (2) joint modeling of reviews and key-value product attributes is challenging; (3) traditional methods easily generate meaningless answers. To tackle above challenges, we propose an adversarial learning based model, named PAAG, which is composed of three components: a questionaware review representation module, a key-value memory network encoding attributes, and a recurrent neural network as a sequence generator. Specifically, we employ a convolutional discriminator to distinguish whether our generated answer matches the facts. To extract the salience part of reviews, an attention-based review reader is proposed to capture the most relevant words given the question.Conducted on a large-scale real-world e-commerce dataset, our extensive experiments verify the effectiveness of each module in our proposed model. Moreover, our experiments show that our model achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations.

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Section: Text Generation Methodsmentioning

confidence: 99%

Product-Aware Answer Generation in E-Commerce Question-Answering

Gao

Zhang

Zhao

et al. 2019

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

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“…allowed because of limitations in the space where the headline appears. The technology of automatic headline generation has the potential to contribute greatly to this domain, and the problems of news headline generation have motivated a wide range of studies (Wang et al, 2018;Chen et al, 2018;Kiyono et al, 2018;Cao et al, 2018;Wang et al, 2019). Table 1 shows sample headlines in three different lengths written by professional editors of a media company for the same news article: The length of the first headline for the digital media is restricted to 10 characters, the second to 13 charac-ters, and the third to 26 characters.…”

Section: トヨタ、エンジン車だけの車種ゼロへ 2025年ごろmentioning

confidence: 99%

A Large-Scale Multi-Length Headline Corpus for Analyzing Length-Constrained Headline Generation Model Evaluation

Hitomi

Taguchi

Tamori

et al. 2019

Proceedings of the 12th International Conference on Natural Language Generation

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Browsing news articles on multiple devices is now possible. The lengths of news article headlines have precise upper bounds, dictated by the size of the display of the relevant device or interface. Therefore, controlling the length of headlines is essential when applying the task of headline generation to news production. However, because there is no corpus of headlines of multiple lengths for a given article, previous research on controlling output length in headline generation has not discussed whether the system outputs could be adequately evaluated without multiple references of different lengths. In this paper, we introduce two corpora, which are Japanese News Corpus (JNC) and JApanese MUlti-Length Headline Corpus (JAMUL), to confirm the validity of previous evaluation settings. The JNC provides common supervision data for headline generation. The JAMUL is a large-scale evaluation dataset for headlines of three different lengths composed by professional editors. We report new findings on these corpora; for example, although the longest length reference summary can appropriately evaluate the existing methods controlling output length, this evaluation setting has several problems.

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“…Inspired by results of "Retrieve, rerank and rewrite" [20] with soft templates Wang and others [35] proposed a new model called BiSET (Bi-directional Selective Encoding with Template for Abstractive Summarization) to enhance soft template usage in text summarization. The work introduces: 1.…”

Section: Rush Chopra and Westonmentioning

confidence: 99%

A literature review of abstractive summarization methods

Shypik

Bidyuk

2019

SRIT

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The paper contains a literature review for automatic abstractive text summarization. The classification of abstractive text summarization methods was considered. Since the emergence of text summarization in the 1950s, techniques for summaries generation were constantly improving, but because the abstractive summarization require extensive language processing, the greatest progress was achieved only recently. Due to the current fast pace of development of both Natural Language Processing in general and Text Summarization in particular, it is essential to analyze the progress in these areas. The paper aims to give a general perspective on both the state-of-the-art and older approaches, while explaining the methods and approaches. Additionally, evaluation results of the research papers are presented.

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BiSET: Bi-directional Selective Encoding with Template for Abstractive Summarization

Cited by 59 publications

References 20 publications

Product-Aware Answer Generation in E-Commerce Question-Answering

Product-Aware Answer Generation in E-Commerce Question-Answering

A Large-Scale Multi-Length Headline Corpus for Analyzing Length-Constrained Headline Generation Model Evaluation

A literature review of abstractive summarization methods

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