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natural-language-processing-2

Scientific Document Summarization

3260 papers • 126 benchmarks • 313 datasets

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(Image credit: Papersgraph)

Benchmarks

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CL-SciSumm

Libraries

Use these libraries to find scientific-document-summarization-2 models and implementations

WING-NUS/scisumm-corpus

3 papers 212

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Lay Summarization

Most implemented papers

ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks

Michihiro Yasunaga, A. R. Fabbri, Rui Zhang, Dragomir R. Radev, Irene Li, Jungo Kasai, Dan Friedman•Tue Jul 16 2019

The first large-scale manually-annotated corpus for scientific papers is developed and released by enabling faster annotation and summarization methods that integrate the authors’ original highlights and the article’s actual impacts on the community are proposed, to create comprehensive, hybrid summaries.

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Introduction Benchmarks Datasets Subtasks Libraries Papers

CL-SciSumm

220

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Overview and Results: CL-SciSumm Shared Task 2019

Michihiro Yasunaga, Dayne Freitag, Dragomir R. Radev, Min-Yen Kan, Muthu Kumar Chandrasekaran•Sun Jun 30 2019

This overview describes the participation and the official results of the CL-SciSumm 2019 Shared Task, organized as a part of the 42nd Annual Conference of the Special Interest Group in Information Retrieval (SIGIR), held in Paris, France in July 2019.

60 0

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The CL-SciSumm Shared Task 2018: Results and Key Insights

Min-Yen Kan, Muthu Kumar Chandrasekaran, Kokil Jaidka, Devanshu Jain•Sun Sep 01 2019

This overview describes the official results of the CL-SciSumm Shared Task 2018 -- the first medium-scale shared task on scientific document summarization in the computational linguistics (CL) domain and compares the participating systems in terms of two evaluation metrics.

56 0

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Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature

Tomas Goldsack, Carolina Scarton, Chenghua Lin•Mon Oct 17 2022

Two novel lay summarisation datasets are presented, PLOS (large-scale) and eLife (medium-scale), each of which contains biomedical journal articles alongside expert-written lay summaries, highlighting differing levels of readability and abstractiveness between datasets that can be leveraged to support the needs of different applications.

92 0

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Hierarchical Attention Graph for Scientific Document Summarization in Global and Local Level

Chenlong Zhao, Xiwen Zhou, Xiaopeng Xie, Yong Zhang•Wed May 15 2024

This paper proposes HAESum, a novel approach utilizing graph neural networks to locally and globally model documents based on their hierarchical discourse structure, and introduces a novel hypergraph self-attention layer to enhance the characterization of high-order inter-sentence relations.

15 0

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Scientific Document Summarization | State-of-the-Art