Title
SlideGen: an abstractive section-based slide generator for scholarly documents
Abstract
ABSTRACTPresentation slides generated from research papers provide summary of the papers primarily to guide talks. Manually generating presentation slides is labor intensive. We propose a method to automatically generate slides for scientific articles based on a corpus of 5000 paper-slide pairs compiled from conference proceedings websites which is the largest dataset used for scholarly article summarization. We generate slides 1) extractively by selecting salient sentences from the paper and 2) abstractively by fine-tuning pre-trained language models to learn the language of slides. The results show the superiority of the extractive models in terms of ROUGE scores. However, abstractive summaries are less verbose and follow the language of the slides by generating phrases rather than full sentences.
Year
DOI
Venue
2021
10.1145/3469096.3474939
DOCENG
Keywords
DocType
Citations 
Text Mining, Summarization, Slide Generation
Conference
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Athar Sefid100.34
Prasenjit Mitra200.34
C. Lee Giles3111541549.48