@inproceedings{kim-2020-using,
title = "Using Pre-Trained Transformer for Better Lay Summarization",
author = "Kim, Seungwon",
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-1.38",
doi = "10.18653/v1/2020.sdp-1.38",
pages = "328--335",
abstract = "In this paper, we tack lay summarization tasks, which aim to automatically produce lay summaries for scientific papers, to participate in the first CL-LaySumm 2020 in SDP workshop at EMNLP 2020. We present our approach of using Pre-training with Extracted Gap-sentences for Abstractive Summarization (PEGASUS; Zhang et al., 2019b) to produce the lay summary and combining those with the extractive summarization model using Bidirectional Encoder Representations from Transformers (BERT; Devlin et al., 2018) and readability metrics that measure the readability of the sentence to further improve the quality of the summary. Our model achieves a remarkable performance on ROUGE metrics, demonstrating the produced summary is more readable while it summarizes the main points of the document.",
}
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<abstract>In this paper, we tack lay summarization tasks, which aim to automatically produce lay summaries for scientific papers, to participate in the first CL-LaySumm 2020 in SDP workshop at EMNLP 2020. We present our approach of using Pre-training with Extracted Gap-sentences for Abstractive Summarization (PEGASUS; Zhang et al., 2019b) to produce the lay summary and combining those with the extractive summarization model using Bidirectional Encoder Representations from Transformers (BERT; Devlin et al., 2018) and readability metrics that measure the readability of the sentence to further improve the quality of the summary. Our model achieves a remarkable performance on ROUGE metrics, demonstrating the produced summary is more readable while it summarizes the main points of the document.</abstract>
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%0 Conference Proceedings
%T Using Pre-Trained Transformer for Better Lay Summarization
%A Kim, Seungwon
%S Proceedings of the First Workshop on Scholarly Document Processing
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F kim-2020-using
%X In this paper, we tack lay summarization tasks, which aim to automatically produce lay summaries for scientific papers, to participate in the first CL-LaySumm 2020 in SDP workshop at EMNLP 2020. We present our approach of using Pre-training with Extracted Gap-sentences for Abstractive Summarization (PEGASUS; Zhang et al., 2019b) to produce the lay summary and combining those with the extractive summarization model using Bidirectional Encoder Representations from Transformers (BERT; Devlin et al., 2018) and readability metrics that measure the readability of the sentence to further improve the quality of the summary. Our model achieves a remarkable performance on ROUGE metrics, demonstrating the produced summary is more readable while it summarizes the main points of the document.
%R 10.18653/v1/2020.sdp-1.38
%U https://aclanthology.org/2020.sdp-1.38
%U https://doi.org/10.18653/v1/2020.sdp-1.38
%P 328-335
Markdown (Informal)
[Using Pre-Trained Transformer for Better Lay Summarization](https://aclanthology.org/2020.sdp-1.38) (Kim, sdp 2020)
ACL