Faithful to the Original: Fact Aware Neural Abstractive Summarization

Understand

Unlike extractive summarization, abstractive summarization has to fuse different parts of the source text, which inclines to create fake facts.

• Our preliminary study reveals nearly 30% of the outputs from a state-of-the-art neural summarization system suffer from this problem.
• While previous abstractive summarization approaches usually focus on the improvement of informativeness, we argue that faithfulness is also a vital prerequisite for a practical abstractive summarization system.
• To avoid generating fake facts in a summary, we leverage open information extraction and dependency parse technologies to extract actual fact descriptions from the source text.