Neural Summarization by Extracting Sentences and Words

Understand

Traditional approaches to extractive summarization rely heavily on human-engineered features.

• In this work we propose a data-driven approach based on neural networks and continuous sentence features.
• We develop a general framework for single-document summarization composed of a hierarchical document encoder and an attention-based extractor.
• This architecture allows us to develop different classes of summarization models which can extract sentences or words.