Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward

Huang, Luyang; Wu, Lingfei; Wang, Lu

doi:10.18653/v1/2020.acl-main.457

Cited by 125 publications

(83 citation statements)

References 30 publications

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“…(2) Reinforcement Learning (RL). RL-based training strategies can incorporate any user-defined metrics, including non-differentiable ones, as rewards to train summarization models [56,62,107,109]. These metrics can be ROUGE [80], BERTScore [154], or saliency and entailment rewards [107] inferred from the Natural Language Inference task [13].…”

Section: Newsroom-summary Newsroom-title Bytecupmentioning

confidence: 99%

Neural Abstractive Text Summarization with Sequence-to-Sequence Models

Shi

Keneshloo

Ramakrishnan

et al. 2021

ACM/IMS Trans. Data Sci.

144

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In the past few years, neural abstractive text summarization with sequence-to-sequence (seq2seq) models have gained a lot of popularity. Many interesting techniques have been proposed to improve seq2seq models, making them capable of handling different challenges, such as saliency, fluency and human readability, and generate high-quality summaries. Generally speaking, most of these techniques differ in one of these three categories: network structure, parameter inference, and decoding/generation. There are also other concerns, such as efficiency and parallelism for training a model. In this article, we provide a comprehensive literature survey on different seq2seq models for abstractive text summarization from the viewpoint of network structures, training strategies, and summary generation algorithms. Several models were first proposed for language modeling and generation tasks, such as machine translation, and later applied to abstractive text summarization. Hence, we also provide a brief review of these models. As part of this survey, we also develop an open source library, namely, Neural Abstractive Text Summarizer (NATS) toolkit, for the abstractive text summarization. An extensive set of experiments have been conducted on the widely used CNN/Daily Mail dataset to examine the effectiveness of several different neural network components. Finally, we benchmark two models implemented in NATS on the two recently released datasets, namely, Newsroom and Bytecup.

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Section: Newsroom-summary Newsroom-title Bytecupmentioning

confidence: 99%

Neural Abstractive Text Summarization with Sequence-to-Sequence Models

Shi

Keneshloo

Ramakrishnan

et al. 2021

ACM/IMS Trans. Data Sci.

144

View full text Add to dashboard Cite

show abstract

“…Although these methods are good at generating readable summaries to a certain extent, the problem of factual inconsistencies persists with them. To alleviate this issue, several new methods (Lebanoff et al, 2020;Huang et al, 2020) has been proposed to generate more factually correct summaries. Few other recent works (Falke et al, 2019;Kryściński et al, 2019;Wang et al, 2020a) have exploited question answering and natural language inference (NLI) models to identify factual coherence in the generated summary.…”

Section: Related Workmentioning

confidence: 99%

NLM at MEDIQA 2021: Transfer Learning-based Approaches for Consumer Question and Multi-Answer Summarization

Yadav¹,

Sarrouti²,

Gupta³

2021

Proceedings of the 20th Workshop on Biomedical Language Processing

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The quest for seeking health information has swamped the web with consumers' healthrelated questions, which makes the need for efficient and reliable question answering systems more pressing. The consumers' questions, however, are very descriptive and contain several peripheral information (like patient's medical history, demographic information, etc.), that are often not required for answering the question. Furthermore, it contributes to the challenges of understanding natural language questions for automatic answer retrieval. Also, it is crucial to provide the consumers with the exact and relevant answers, rather than the entire pool of answer documents to their question. One of the cardinal tasks in achieving robust consumer health question answering systems is the question summarization and multi-document answer summarization. This paper describes the participation of the U.S. National Library of Medicine (NLM) in Consumer Question and Multi-Answer Summarization tasks of the MEDIQA 2021 challenge at NAACL-BioNLP workshop. In this work, we exploited the capabilities of pre-trained transformer models and introduced a transfer learning approach for the abstractive Question Summarization and extractive Multi-Answer Summarization tasks by first pre-training our model on a task-specific summarization dataset followed by fine-tuning it for both the tasks via incorporating medical entities. We achieved the second, sixth and the fourth position for the Question Summarization task in terms ROUGE-1, ROUGE-2 and ROUGE-L scores respectively.

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“…In contrast, early abstractive models using sentence-fusion and paraphrasing (Filippova and Strube, 2008;Banerjee et al, 2015;Bing et al, 2015) achieve less success. Inspired by the recent success of single-document abstractive models (See et al, 2017;Paulus et al, 2018;Gehrmann et al, 2018;Huang et al, 2020), some works (Liu et al, 2018;Zhang et al, 2018) try to transfer single-document models to multi-document settings to alleviate the limitations of small-scale datasets. Specifically, Liu et al (2018) defines Wikipedia generation problem and contributes the large-scale WikiSum dataset.…”

Section: Multi-document Summarizationmentioning

confidence: 99%

TWAG: A Topic-Guided Wikipedia Abstract Generator

Zhu¹,

Tu²,

Shi³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Wikipedia abstract generation aims to distill a Wikipedia abstract from web sources and has met significant success by adopting multidocument summarization techniques. However, previous works generally view the abstract as plain text, ignoring the fact that it is a description of a certain entity and can be decomposed into different topics. In this paper, we propose a two-stage model TWAG that guides the abstract generation with topical information. First, we detect the topic of each input paragraph with a classifier trained on existing Wikipedia articles to divide input documents into different topics. Then, we predict the topic distribution of each abstract sentence, and decode the sentence from topic-aware representations with a Pointer-Generator network. We evaluate our model on the Wi-kiCatSum dataset, and the results show that TWAG outperforms various existing baselines and is capable of generating comprehensive abstracts. Our code and dataset can be accessed at https://github.com/THU-KEG/TWAG

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Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward

Cited by 125 publications

References 30 publications

Neural Abstractive Text Summarization with Sequence-to-Sequence Models

Neural Abstractive Text Summarization with Sequence-to-Sequence Models

NLM at MEDIQA 2021: Transfer Learning-based Approaches for Consumer Question and Multi-Answer Summarization

TWAG: A Topic-Guided Wikipedia Abstract Generator

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