@inproceedings{chaturvedi-etal-2020-divide,
title = "Divide and Conquer: From Complexity to Simplicity for Lay Summarization",
author = "Chaturvedi, Rochana and
{Saachi} and
Dhani, Jaspreet Singh and
Joshi, Anurag and
Khanna, Ankush and
Tomar, Neha and
Duari, Swagata and
Khurana, Alka and
Bhatnagar, Vasudha",
editor = "Chandrasekaran, Muthu Kumar and
de Waard, Anita and
Feigenblat, Guy and
Freitag, Dayne and
Ghosal, Tirthankar and
Hovy, Eduard and
Knoth, Petr and
Konopnicki, David and
Mayr, Philipp and
Patton, Robert M. and
Shmueli-Scheuer, Michal",
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-1.40",
doi = "10.18653/v1/2020.sdp-1.40",
pages = "344--355",
abstract = "We describe our approach for the 1st Computational Linguistics Lay Summary Shared Task CL-LaySumm20. The task is to produce non-technical summaries of scholarly documents. The summary should be within easy grasp of a layman who may not be well versed with the domain of the research article. We propose a two step divide-and-conquer approach. First, we judiciously select segments of the documents that are not overly pedantic and are likely to be of interest to the laity, and over-extract sentences from each segment using an unsupervised network based method. Next, we perform abstractive summarization on these extractions and systematically merge the abstractions. We run ablation studies to establish that each step in our pipeline is critical for improvement in the quality of lay summary. Our approach leverages state-of-the-art pre-trained deep neural network based models as zero-shot learners to achieve high scores on the task.",
}
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<abstract>We describe our approach for the 1st Computational Linguistics Lay Summary Shared Task CL-LaySumm20. The task is to produce non-technical summaries of scholarly documents. The summary should be within easy grasp of a layman who may not be well versed with the domain of the research article. We propose a two step divide-and-conquer approach. First, we judiciously select segments of the documents that are not overly pedantic and are likely to be of interest to the laity, and over-extract sentences from each segment using an unsupervised network based method. Next, we perform abstractive summarization on these extractions and systematically merge the abstractions. We run ablation studies to establish that each step in our pipeline is critical for improvement in the quality of lay summary. Our approach leverages state-of-the-art pre-trained deep neural network based models as zero-shot learners to achieve high scores on the task.</abstract>
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%0 Conference Proceedings
%T Divide and Conquer: From Complexity to Simplicity for Lay Summarization
%A Chaturvedi, Rochana
%A Dhani, Jaspreet Singh
%A Joshi, Anurag
%A Khanna, Ankush
%A Tomar, Neha
%A Duari, Swagata
%A Khurana, Alka
%A Bhatnagar, Vasudha
%Y Chandrasekaran, Muthu Kumar
%Y de Waard, Anita
%Y Feigenblat, Guy
%Y Freitag, Dayne
%Y Ghosal, Tirthankar
%Y Hovy, Eduard
%Y Knoth, Petr
%Y Konopnicki, David
%Y Mayr, Philipp
%Y Patton, Robert M.
%Y Shmueli-Scheuer, Michal
%A Saachi
%S Proceedings of the First Workshop on Scholarly Document Processing
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F chaturvedi-etal-2020-divide
%X We describe our approach for the 1st Computational Linguistics Lay Summary Shared Task CL-LaySumm20. The task is to produce non-technical summaries of scholarly documents. The summary should be within easy grasp of a layman who may not be well versed with the domain of the research article. We propose a two step divide-and-conquer approach. First, we judiciously select segments of the documents that are not overly pedantic and are likely to be of interest to the laity, and over-extract sentences from each segment using an unsupervised network based method. Next, we perform abstractive summarization on these extractions and systematically merge the abstractions. We run ablation studies to establish that each step in our pipeline is critical for improvement in the quality of lay summary. Our approach leverages state-of-the-art pre-trained deep neural network based models as zero-shot learners to achieve high scores on the task.
%R 10.18653/v1/2020.sdp-1.40
%U https://aclanthology.org/2020.sdp-1.40
%U https://doi.org/10.18653/v1/2020.sdp-1.40
%P 344-355
Markdown (Informal)
[Divide and Conquer: From Complexity to Simplicity for Lay Summarization](https://aclanthology.org/2020.sdp-1.40) (Chaturvedi et al., sdp 2020)
ACL
- Rochana Chaturvedi, Saachi, Jaspreet Singh Dhani, Anurag Joshi, Ankush Khanna, Neha Tomar, Swagata Duari, Alka Khurana, and Vasudha Bhatnagar. 2020. Divide and Conquer: From Complexity to Simplicity for Lay Summarization. In Proceedings of the First Workshop on Scholarly Document Processing, pages 344–355, Online. Association for Computational Linguistics.