Extractive Research Slide Generation Using Windowed Labeling Ranking

Athar Sefid, Prasenjit Mitra, Jian Wu, C Lee Giles


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.
Anthology ID:
2021.sdp-1.11
Volume:
Proceedings of the Second Workshop on Scholarly Document Processing
Month:
June
Year:
2021
Address:
Online
Venues:
NAACL | sdp
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
91–96
Language:
URL:
https://aclanthology.org/2021.sdp-1.11
DOI:
10.18653/v1/2021.sdp-1.11
Bibkey:
Cite (ACL):
Athar Sefid, Prasenjit Mitra, Jian Wu, and C Lee Giles. 2021. Extractive Research Slide Generation Using Windowed Labeling Ranking. In Proceedings of the Second Workshop on Scholarly Document Processing, pages 91–96, Online. Association for Computational Linguistics.
Cite (Informal):
Extractive Research Slide Generation Using Windowed Labeling Ranking (Sefid et al., sdp 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.sdp-1.11.pdf
Code
 atharsefid/Extractive_Research_Slide_Generation_Using_Windowed_Labeling_Ranking
Data
5k_presetation_slides