Extractive Summarizer for Scholarly Articles
This work addresses the challenge of efficiently summarizing scholarly articles for researchers and readers, but it is incremental as it builds on existing extractive methods with a specific labeling approach.
The authors tackled the problem of summarizing long scientific papers by developing an extractive method that uses author-provided presentation slides as a gold standard for labeling sentences, achieving an improvement of at least 4 ROUGE1-Recall points.
We introduce an extractive method that will summarize long scientific papers. Our model uses presentation slides provided by the authors of the papers as the gold summary standard to label the sentences. The sentences are ranked based on their novelty and their importance as estimated by deep neural networks. Our window-based extractive labeling of sentences results in the improvement of at least 4 ROUGE1-Recall points.