Human-Like Summaries from Heterogeneous and Time-Windowed Software Development Artefacts

Alghamdi, Mahfouth; Wagner, Markus

doi:10.1007/978-3-030-58115-2_23

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…Furthermore, putting the collected data into a coherent text from multiple documents to make a cohesive summary [29]. Summarization on news [30], scientific publications [31], emails [32], [33], product reviews [34], lecture feedback [35], [36], Wikipedia article generation [37], medical documents [38], and software project activities [39] are just a few examples of real-world applications for the multi-document summarization task [28]. Summarization can be done in two ways: abstractive or extractive.…”

Section: Structure Of Atsmentioning

confidence: 99%

A Survey of Automatic Text Summarization: Progress, Process and Challenges

et al. 2021

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Section: Structure Of Atsmentioning

confidence: 99%

A Survey of Automatic Text Summarization: Progress, Process and Challenges

et al. 2021

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“…In the future, datasets with documents of rich diversity are desperately required to promote the research of multi-document summarization. Meanwhile, according to application requirements, datasets in cross-domains are ought to be collected, for example, medical records or dialogue summarization [86], email summarization [124,145], code summarization [80,108], software project activities summarization [2], legal documents summarization [57]. The development of large-scale cross-task datasets will facilitate multi-task learning [34,135].…”

Section: Creating More Datasets For Multi-document Summarizationmentioning

confidence: 99%

“…Multi-document summarization task enjoys a wide range of real world applications, including summarization on news [35], scientific publications [141], emails [20,145], product reviews [40], lecture feedback [77,78], Wikipedia articles generation [73], medical documents [1] and software project activities [2]. Recently, multi-document summarization technology has also received a great amount of attention in the industry.…”

Section: Introductionmentioning

confidence: 99%

Multi-document Summarization via Deep Learning Techniques: A Survey

Ma¹,

Zhang²,

Guo³

et al. 2020

Preprint

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Multi-document summarization (MDS) is an effective tool for information aggregation which generates an informative and concise summary from a cluster of topic-related documents. Our survey structurally overviews the recent deep learning based multi-document summarization models via a proposed taxonomy and it is the first of its kind. Particularly, we propose a novel mechanism to summarize the design strategies of neural networks and conduct a comprehensive summary of the state-of-the-art. We highlight the differences among various objective functions which are rarely discussed in the existing literature. Finally, we propose several future directions pertaining to this new and exciting development of the field.

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Multi-document Summarization via Deep Learning Techniques: A Survey

Zhang

Guo

et al. 2022

ACM Comput. Surv.

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Multi-document summarization (MDS) is an effective tool for information aggregation that generates an informative and concise summary from a cluster of topic-related documents. Our survey, the first of its kind, systematically overviews the recent deep learning based MDS models. We propose a novel taxonomy to summarize the design strategies of neural networks and conduct a comprehensive summary of the state-of-the-art. We highlight the differences between various objective functions that are rarely discussed in the existing literature. Finally, we propose several future directions pertaining to this new and exciting field.

show abstract

Human-Like Summaries from Heterogeneous and Time-Windowed Software Development Artefacts

Cited by 3 publications

References 13 publications

A Survey of Automatic Text Summarization: Progress, Process and Challenges

A Survey of Automatic Text Summarization: Progress, Process and Challenges

Multi-document Summarization via Deep Learning Techniques: A Survey

Multi-document Summarization via Deep Learning Techniques: A Survey

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