@inproceedings{sefid-etal-2021-extractive,
title = "Extractive Research Slide Generation Using Windowed Labeling Ranking",
author = "Sefid, Athar and
Mitra, Prasenjit and
Wu, Jian and
Giles, C Lee",
booktitle = "Proceedings of the Second Workshop on Scholarly Document Processing",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.sdp-1.11",
doi = "10.18653/v1/2021.sdp-1.11",
pages = "91--96",
abstract = "Presentation slides generated from original research papers provide an efficient form to present research innovations. Manually generating presentation slides is labor-intensive. We propose a method to automatically generates slides for scientific articles based on a corpus of 5000 paper-slide pairs compiled from conference proceedings websites. The sentence labeling module of our method is based on SummaRuNNer, a neural sequence model for extractive summarization. Instead of ranking sentences based on semantic similarities in the whole document, our algorithm measures the importance and novelty of sentences by combining semantic and lexical features within a sentence window. Our method outperforms several baseline methods including SummaRuNNer by a significant margin in terms of ROUGE score.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sefid-etal-2021-extractive">
<titleInfo>
<title>Extractive Research Slide Generation Using Windowed Labeling Ranking</title>
</titleInfo>
<name type="personal">
<namePart type="given">Athar</namePart>
<namePart type="family">Sefid</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Prasenjit</namePart>
<namePart type="family">Mitra</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jian</namePart>
<namePart type="family">Wu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">C</namePart>
<namePart type="given">Lee</namePart>
<namePart type="family">Giles</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2021-jun</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Second Workshop on Scholarly Document Processing</title>
</titleInfo>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Online</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Presentation slides generated from original research papers provide an efficient form to present research innovations. Manually generating presentation slides is labor-intensive. We propose a method to automatically generates slides for scientific articles based on a corpus of 5000 paper-slide pairs compiled from conference proceedings websites. The sentence labeling module of our method is based on SummaRuNNer, a neural sequence model for extractive summarization. Instead of ranking sentences based on semantic similarities in the whole document, our algorithm measures the importance and novelty of sentences by combining semantic and lexical features within a sentence window. Our method outperforms several baseline methods including SummaRuNNer by a significant margin in terms of ROUGE score.</abstract>
<identifier type="citekey">sefid-etal-2021-extractive</identifier>
<identifier type="doi">10.18653/v1/2021.sdp-1.11</identifier>
<location>
<url>https://aclanthology.org/2021.sdp-1.11</url>
</location>
<part>
<date>2021-jun</date>
<extent unit="page">
<start>91</start>
<end>96</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Extractive Research Slide Generation Using Windowed Labeling Ranking
%A Sefid, Athar
%A Mitra, Prasenjit
%A Wu, Jian
%A Giles, C. Lee
%S Proceedings of the Second Workshop on Scholarly Document Processing
%D 2021
%8 jun
%I Association for Computational Linguistics
%C Online
%F sefid-etal-2021-extractive
%X Presentation slides generated from original research papers provide an efficient form to present research innovations. Manually generating presentation slides is labor-intensive. We propose a method to automatically generates slides for scientific articles based on a corpus of 5000 paper-slide pairs compiled from conference proceedings websites. The sentence labeling module of our method is based on SummaRuNNer, a neural sequence model for extractive summarization. Instead of ranking sentences based on semantic similarities in the whole document, our algorithm measures the importance and novelty of sentences by combining semantic and lexical features within a sentence window. Our method outperforms several baseline methods including SummaRuNNer by a significant margin in terms of ROUGE score.
%R 10.18653/v1/2021.sdp-1.11
%U https://aclanthology.org/2021.sdp-1.11
%U https://doi.org/10.18653/v1/2021.sdp-1.11
%P 91-96
Markdown (Informal)
[Extractive Research Slide Generation Using Windowed Labeling Ranking](https://aclanthology.org/2021.sdp-1.11) (Sefid et al., sdp 2021)
ACL