A Neural Attention Model for Abstractive Sentence Summarization

Understand

Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build.

• In this work, we propose a fully data-driven approach to abstractive sentence summarization.
• Our method utilizes a local attention-based model that generates each word of the summary conditioned on the input sentence.
• While the model is structurally simple, it can easily be trained end-to-end and scales to a large amount of training data.