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

TL;DR

This paper introduces WSL-DS, a weakly supervised learning method using distant supervision for query-focused multi-document summarization, achieving state-of-the-art results without requiring labeled datasets.

Contribution

It proposes a novel weakly supervised approach leveraging pre-trained models and distant supervision to improve multi-document summarization.

Findings

Sets new state-of-the-art results on DUC datasets.

Effective in reducing training complexity for multi-document summarization.

Utilizes pre-trained sentence similarity models for weak reference generation.

Abstract

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.