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.
|Original language||English (US)|
|Number of pages||6|
|Journal||CEUR Workshop Proceedings|
|Publication status||Published - Jan 1 2019|
|Event||3rd International Workshop on Capturing Scientific Knowledge, SciKnow 2019 - Marina del Rey, United States|
Duration: Nov 19 2019 → …