AttSum: Joint Learning of Focusing and Summarization with Neural Attention

Understand

Query relevance ranking and sentence saliency ranking are the two main tasks in extractive query-focused summarization.

• Previous supervised summarization systems often perform the two tasks in isolation.
• However, since reference summaries are the trade-off between relevance and saliency, using them as supervision, neither of the two rankers could be trained well.
• This paper proposes a novel summarization system called AttSum, which tackles the two tasks jointly.