@inproceedings{mishra-etal-2020-iitp,
title = "{IITP}-{AI}-{NLP}-{ML}@ {CL}-{S}ci{S}umm 2020, {CL}-{L}ay{S}umm 2020, {L}ong{S}umm 2020",
author = "Mishra, Santosh Kumar and
Kundarapu, Harshavardhan and
Saini, Naveen and
Saha, Sriparna and
Bhattacharyya, Pushpak",
booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.sdp-1.30",
doi = "10.18653/v1/2020.sdp-1.30",
pages = "270--276",
abstract = "The publication rate of scientific literature increases rapidly, which poses a challenge for researchers to keep themselves updated with new state-of-the-art. Scientific document summarization solves this problem by summarizing the essential fact and findings of the document. In the current paper, we present the participation of IITP-AI-NLP-ML team in three shared tasks, namely, CL-SciSumm 2020, LaySumm 2020, LongSumm 2020, which aims to generate medium, lay, and long summaries of the scientific articles, respectively. To solve CL-SciSumm 2020 and LongSumm 2020 tasks, three well-known clustering techniques are used, and then various sentence scoring functions, including textual entailment, are used to extract the sentences from each cluster for a summary generation. For LaySumm 2020, an encoder-decoder based deep learning model has been utilized. Performances of our developed systems are evaluated in terms of ROUGE measures on the associated datasets with the shared task.",
}
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%0 Conference Proceedings
%T IITP-AI-NLP-ML@ CL-SciSumm 2020, CL-LaySumm 2020, LongSumm 2020
%A Mishra, Santosh Kumar
%A Kundarapu, Harshavardhan
%A Saini, Naveen
%A Saha, Sriparna
%A Bhattacharyya, Pushpak
%S Proceedings of the First Workshop on Scholarly Document Processing
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F mishra-etal-2020-iitp
%X The publication rate of scientific literature increases rapidly, which poses a challenge for researchers to keep themselves updated with new state-of-the-art. Scientific document summarization solves this problem by summarizing the essential fact and findings of the document. In the current paper, we present the participation of IITP-AI-NLP-ML team in three shared tasks, namely, CL-SciSumm 2020, LaySumm 2020, LongSumm 2020, which aims to generate medium, lay, and long summaries of the scientific articles, respectively. To solve CL-SciSumm 2020 and LongSumm 2020 tasks, three well-known clustering techniques are used, and then various sentence scoring functions, including textual entailment, are used to extract the sentences from each cluster for a summary generation. For LaySumm 2020, an encoder-decoder based deep learning model has been utilized. Performances of our developed systems are evaluated in terms of ROUGE measures on the associated datasets with the shared task.
%R 10.18653/v1/2020.sdp-1.30
%U https://aclanthology.org/2020.sdp-1.30
%U https://doi.org/10.18653/v1/2020.sdp-1.30
%P 270-276
Markdown (Informal)
[IITP-AI-NLP-ML@ CL-SciSumm 2020, CL-LaySumm 2020, LongSumm 2020](https://aclanthology.org/2020.sdp-1.30) (Mishra et al., sdp 2020)
ACL