  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.
Then, we iteratively train our summarization model on each single-document to
alleviate the computational complexity issue that occurs while training neural
summarization models in multiple documents (i.e., long sequences) at once.
Experimental results in Document Understanding Conferences (DUC) datasets show
that our proposed approach sets a new state-of-the-art result in terms of
various evaluation metrics.
