Team MLU@CL-SciSumm20: Methods for Computational Linguistics Scientific Citation Linkage

Huang, Rong; Крылова, К. В.

doi:10.18653/v1/2020.sdp-1.32

Cited by 2 publications

(1 citation statement)

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“…This paper has presented three shared tasks, including CL-SciSumm 2020 [19], CL-LaySumm 2020 [20], and a third job called LongSumm 2020 [21]. On the NVIDIA T4 Tensor Core GPU, we fine-tune BERTBASE [22] on Masked Language Modeling and Sequence Classification tasks using the Transformer library2 for 2-6 epochs (with early stopping) before utilizing Adam to optimize our models.…”

Section: Implementation and Resultsmentioning

confidence: 99%

Data Mining Oriented Automatic Scientific Documents Summarization

Kalyani¹,

Wankhede²,

Shahanaz³

2023

IJRITCC

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The scientific research process usually begins with an examination of the advanced, which may include voluminous publications. Summarizing scientific articles can assist researchers in their research by speeding up the research process. The summary of scientific articles differs from the abstract text in general due to its specific structure and the inclusion of cited sentences. Most of the important information in scientific articles is presented in tables, statistics, and algorithm pseudocode. These features, however, rarely appear in the standard text. Therefore, a number of methods that consider the value of the structure of a scientific article have been suggested that improve the standard of the produced summary. This paper makes use of clustering algorithms to handle CL- SciSumm 2020 and longsumm 2020 tasks for summarization of scientific documents. There are three well-known clustering algorithms that are employed to tackle CL- SciSumm 2020 and LongSumm 2020 tasks, and several sentences recording functions, with textual deduction, are used to retrieved phrases from each cluster to generate summary.

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Section: Implementation and Resultsmentioning

confidence: 99%

Data Mining Oriented Automatic Scientific Documents Summarization

Kalyani¹,

Wankhede²,

Shahanaz³

2023

IJRITCC

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Overview and Insights from the Shared Tasks at Scholarly Document Processing 2020: CL-SciSumm, LaySumm and LongSumm

Chandrasekaran¹,

Feigenblat²,

Hovy³

et al. 2020

Proceedings of the First Workshop on Scholarly Document Processing

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We present the results of three Shared Tasks held at the Scholarly Document Processing Workshop at EMNLP2020: CL-SciSumm, LaySumm and LongSumm. We report on each of the tasks, which received 18 submissions in total, with some submissions addressing two or three of the tasks. In summary, the quality and quantity of the submissions show that there is ample interest in scholarly document summarization, and the state of the art in this domain is at a midway point between being an impossible task and one that is fully resolved.

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Team MLU@CL-SciSumm20: Methods for Computational Linguistics Scientific Citation Linkage

Cited by 2 publications

References 1 publication

Data Mining Oriented Automatic Scientific Documents Summarization

Data Mining Oriented Automatic Scientific Documents Summarization

Overview and Insights from the Shared Tasks at Scholarly Document Processing 2020: CL-SciSumm, LaySumm and LongSumm

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