A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents
This work addresses the challenge of summarizing long documents for researchers and practitioners, representing a novel method for a known bottleneck in abstractive summarization.
The authors tackled the problem of abstractive summarization for long documents, such as research papers, by proposing a discourse-aware attention model, which significantly outperformed state-of-the-art models on two large-scale datasets of scientific papers.
Neural abstractive summarization models have led to promising results in summarizing relatively short documents. We propose the first model for abstractive summarization of single, longer-form documents (e.g., research papers). Our approach consists of a new hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. Empirical results on two large-scale datasets of scientific papers show that our model significantly outperforms state-of-the-art models.