Personalizing News Headlines with Retrieval-Augmented Generation

Jiajing Wan, Samia Touileb, Lubos Steskal, Lilja Øvrelid

Keywords: retrieval-augmented generation, text-to-text generation

Abstract:

We focus on personalized news headline generation, where we aim to improve headline generation by extending the generation context to incorporate the news reading history of users. In particular, we study a RAG-LLM-based system that customizes news headlines with user histories to improve news headline personalization. Our experiments show that our approach not only produces better headlines for specific users, but also makes the generated headlines closer to the original headlines.
We experiment with different retrievers and analyze the generated outputs through systematic comparisons with both original and rewritten headlines. These analyses provide insights into the role of retrieval and personalization in headline generation, highlighting how the user history contributes to meaningful improvement while remaining aligned with original headlines.

Email Sharing: We authorize the sharing of all author emails with Program Chairs.

Data Release: We authorize the release of our submission and author names to the public in the event of acceptance.

Submission Number: 16