WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization

Understand

In the Query Focused Multi-Document Summarization (QF-MDS) task, a set of documents and a query are given where the goal is to generate a summary from these documents based on the given query.

• However, one major challenge for this task is the lack of availability of labeled training datasets.
• To overcome this issue, in this paper, we propose a novel weakly supervised learning approach via utilizing distant supervision.
• In particular, we use datasets similar to the target dataset as the training data where we leverage pre-trained sentence similarity models to generate the weak reference summary of each individual document in a document set from the multi-document gold reference summaries.