We describe our approach for automatically generating presentation
slides for scientific papers using deep neural networks. Such
slides can help authors have a starting point for their slide generation
process. Extractive summarization techniques are applied to
rank and select important sentences from the original document.
Previous work identified important sentences based only on a limited
number of features that were extracted from the position and
structure of sentences in the paper. Our method extends previous
work by (1) extracting a more comprehensive list of surface features,
(2) considering semantic or meaning of the sentence, and (3)
using context around the current sentence to rank the sentences.
Once, the sentences are ranked, salient sentences are selected using
Integer Linear Programming (ILP). Our results show the efficacy of
our model for summarization and the slide generation task.
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Extractive Research Slide Generation UsingWindowed Labeling Ranking
Presentation slides describing the content of
scientific and technical papers are an efficient
and effective way to present that work. However,
manually generating presentation slides
is labor intensive. We propose a method to
automatically generate slides for scientific papers
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 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.
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- Award ID(s):
- 1823288
- NSF-PAR ID:
- 10272160
- Date Published:
- Journal Name:
- Proceedings of the Second Workshop on ScProceedings of the Second Workshop on Scholarly Document Processing, 2021 Association for Computational Linguistics
- Page Range / eLocation ID:
- 91-96
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
