FAR-ASS: Fact-aware reinforced abstractive sentence summarization

Zhang, Mengli; Zhou, Gang; Yu, Wanting; Liu, Wenfen

doi:10.1016/j.ipm.2020.102478

Cited by 28 publications

(9 citation statements)

References 8 publications

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“…Zhang et al [ 107 ] proposed a fact-aware reinforced ABS model (FAR-ASS). They also employed the OpenIE and dependency parser tools to extract fact descriptions of the input document.…”

Section: Methodologiesmentioning

confidence: 99%

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A Comprehensive Survey of Abstractive Text Summarization Based on Deep Learning

Zhang¹,

Zhou²,

Yu³

et al. 2022

Computational Intelligence and Neuroscience

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With the rapid development of the Internet, the massive amount of web textual data has grown exponentially, which has brought considerable challenges to downstream tasks, such as document management, text classification, and information retrieval. Automatic text summarization (ATS) is becoming an extremely important means to solve this problem. The core of ATS is to mine the gist of the original text and automatically generate a concise and readable summary. Recently, to better balance and develop these two aspects, deep learning (DL)-based abstractive summarization models have been developed. At present, for ATS tasks, almost all state-of-the-art (SOTA) models are based on DL architecture. However, a comprehensive literature survey is still lacking in the field of DL-based abstractive text summarization. To fill this gap, this paper provides researchers with a comprehensive survey of DL-based abstractive summarization. We first give an overview of abstractive summarization and DL. Then, we summarize several typical frameworks of abstractive summarization. After that, we also give a comparison of several popular datasets that are commonly used for training, validation, and testing. We further analyze the performance of several typical abstractive summarization systems on common datasets. Finally, we highlight some open challenges in the abstractive summarization task and outline some future research trends. We hope that these explorations will provide researchers with new insights into DL-based abstractive summarization.

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“…Zhang et al [ 107 ] proposed a fact-aware reinforced ABS model (FAR-ASS). They also employed the OpenIE and dependency parser tools to extract fact descriptions of the input document.…”

Section: Methodologiesmentioning

confidence: 99%

“…In the training phase, they adopted a reinforcement learning strategy based on fact correctness scores to train the summarization model. The overall framework of the FAR-ASS model is shown in Figure 14 [ 107 ].…”

Section: Methodologiesmentioning

confidence: 99%

A Comprehensive Survey of Abstractive Text Summarization Based on Deep Learning

Zhang¹,

Zhou²,

Yu³

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

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“…Table 15 shows a few false descriptions and their errors, which are highlighted in orange with corresponding explanations. Although our research did not design any mechanism to control the repetitive texts and factual information, these problems were addressed by some approaches such as Pointer-Generator Networks [39], Global Encoding [108], reinforcement learning [109], rule-based/heuristic transformations [110,111], and graph attention [112].…”

Section: Error Analysismentioning

confidence: 99%

WikiDes: A Wikipedia-based dataset for generating short descriptions from paragraphs

Rahman²,

Majumder³

et al. 2023

Information Fusion

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“…However, it is challenging to predict which sentences in the text will be used in abstractive systems and how they will be reinterpreted later. This is a significant disadvantage compared to subtractive summarisation systems [5,9–12].…”

Section: Introductionmentioning

confidence: 99%

A new multi-document summarisation approach using saplings growing-up optimisation algorithms: Simultaneously optimised coverage and diversity

Hark

Uçkan

Karcı

2022

Journal of Information Science

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Automatic text summarisation is obtaining a subset that accurately represents the main text. A quality summary should contain the maximum amount of information while avoiding redundant information. Redundancy is a severe deficiency that causes unnecessary repetition of information within sentences and should not occur in summarisation studies. Although many optimisation-based text summarisation methods have been proposed in recent years, there exists a lack of research on the simultaneous optimisation of scope and redundancy. In this context, this study presents an approach in which maximum coverage and minimum redundancy, which form the two key features of a rich summary, are modelled as optimisation targets. In optimisation-based text summarisation studies, different conflicting objectives are generally weighted or formulated and transformed into single-objective problems. However, this transformation can directly affect the quality of the solution. In this study, the optimisation goals are met simultaneously without transformation or formulation. In addition, the multi-objective saplings growing-up algorithm (MO-SGuA) is implemented and modified for text summarisation. The presented approach, called Pareto optimal, achieves an optimal solution with simultaneous optimisation. Experimentation with the MO-SGuA method was tested using open-access (document understanding conference; DUC) data sets. Performance success of the MO-SGuA approach was calculated using the recall-oriented understudy for gisting evaluation (ROUGE) metrics and then compared with the competitive practices used in the literature. Testing achieved a 26.6% summarisation result for the ROUGE-2 metric and 65.96% for ROUGE-L, which represents an improvement of 11.17% and 20.54%, respectively. The experimental results showed that good-quality summaries were achieved using the proposed approach.

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FAR-ASS: Fact-aware reinforced abstractive sentence summarization

Cited by 28 publications

References 8 publications

A Comprehensive Survey of Abstractive Text Summarization Based on Deep Learning

A Comprehensive Survey of Abstractive Text Summarization Based on Deep Learning

WikiDes: A Wikipedia-based dataset for generating short descriptions from paragraphs

A new multi-document summarisation approach using saplings growing-up optimisation algorithms: Simultaneously optimised coverage and diversity

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