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

Proceedings of the 28th International Conference on Computational Linguistics, pages 5647–5654 Barcelona, Spain (Online), December 8-13, 2020 5647 WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization Md Tahmid Rahman Laskar York University Toronto, Ontario, Canada tahmedge@cse.yorku.ca Enamul Hoque York University Toronto, Ontario, Canada enamulh@yorku.ca Jimmy Xiangji Huang York University Toronto, Ontario, Canada jhuang@yorku.ca 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 on the Document Understanding Conferences (DUC) datasets show that our proposed approach sets a new state-of-the-art result in terms of various evaluation metrics. 1 Introduction With the rapid growth of textual documents on the internet, accessing information from the web has become a challenging issue (Yao et al., 2017). Often users want the summary of a topic from various sources to fulfill their information needs (Feigenblat et al., 2017). The QF-MDS task deals with such problems where the goal is to summarize a set of documents to answer a given query. In the QF-MDS task, the summaries generated by the summarizer can be either extractive or abstrac- tive (Yao et al., 2017; Kulkarni et al., 2020). An extractive summarizer extracts relevant text spans from the source document(s), whereas an abstractive summarizer generates a summary in natural language that may contain some words which did not appear in the source document(s) (Rush et al., 2015; Nallapati et al., 2016; Nema et al., 2017). With the rising popularity of virtual assistants in recent years, there is a growing interest to integrate abstractive summarization capabilities in these systems for natural response generation (Nishida et al., 2019). One major challenge for the QF-MDS task is that the datasets used for such tasks do not contain any labeled training data (Xu and Lapata, 2020). Therefore, neural summarization models that leverage supervised training cannot be used in these datasets. Note that for other related tasks (Allan et al., 2003; Liu et al., 2008; Miao et al., 2012), how to reduce the demands for labeling the data and how to leverage unlabeled data were also identified as a major challenge. While using datasets similar to the target dataset as the training data for the QF-MDS task, we find that these datasets only contain multi-document gold …

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