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The Query Focused Text Summarization (QFTS) task aims at building systemsthat generate the summary of the text document(s) based on the given query. Akey challenge in addressing this task is the lack of large labeled data fortraining the summarization model. In this paper, we address this challenge byexploring a series of domain adaptation techniques. Given the recent success ofpre-trained transformer models in a wide range of natural language processingtasks, we utilize such models to generate abstractive summaries for the QFTStask for both single-document and multi-document scenarios. For domainadaptation, we apply a variety of techniques using pre-trainedtransformer-based summarization models including transfer learning, weaklysupervised learning, and distant supervision. Extensive experiments on sixdatasets show that our proposed approach is very effective in generatingabstractive summaries for the QFTS task while setting a new state-of-the-artresult in several datasets across a set of automatic and human evaluationmetrics.

Domain Adaptation with Pre-trained Transformers for Query Focused Abstractive Text Summarization