Interpretable Multi-Headed Attention for Abstractive Summarization at Controllable Lengths

Understand

Abstractive summarization at controllable lengths is a challenging task in natural language processing.

• It is even more challenging for domains where limited training data is available or scenarios in which the length of the summary is not known beforehand.
• At the same time, when it comes to trusting machine-generated summaries, explaining how a summary was constructed in human-understandable terms may be critical.
• We propose Multi-level Summarizer (MLS), a supervised method to construct abstractive summaries of a text document at controllable lengths.