  Query-focused summarization (QFS) is a challenging task in natural language
processing that generates summaries to address specific queries. The broader
field of Generative Information Retrieval (Gen-IR) aims to revolutionize
information extraction from vast document corpora through generative
approaches, encompassing Generative Document Retrieval (GDR) and Grounded
Answer Retrieval (GAR). This paper highlights the role of QFS in Grounded
Answer Generation (GAR), a key subdomain of Gen-IR that produces human-readable
answers in direct correspondence with queries, grounded in relevant documents.
In this study, we propose QontSum, a novel approach for QFS that leverages
contrastive learning to help the model attend to the most relevant regions of
the input document. We evaluate our approach on a couple of benchmark datasets
for QFS and demonstrate that it either outperforms existing state-of-the-art or
exhibits a comparable performance with considerably reduced computational cost
through enhancements in the fine-tuning stage, rather than relying on
large-scale pre-training experiments, which is the focus of current SOTA.
Moreover, we conducted a human study and identified improvements in the
relevance of generated summaries to the posed queries without compromising
fluency. We further conduct an error analysis study to understand our model's
limitations and propose avenues for future research.
