SumRecom: A Personalized Summarization Approach by Learning from Users' Feedback
This addresses the impracticality of uniform summaries for all users in multi-document summarization, offering a personalized solution, though it is incremental as it builds on existing interactive and reinforcement learning methods.
The paper tackles the problem of generating user-specific summaries by proposing SumRecom, which learns from user feedback to personalize summaries without needing reference summaries, achieving superior performance in evaluations.
Existing multi-document summarization approaches produce a uniform summary for all users without considering individuals' interests, which is highly impractical. Making a user-specific summary is a challenging task as it requires: i) acquiring relevant information about a user; ii) aggregating and integrating the information into a user-model; and iii) utilizing the provided information in making the personalized summary. Therefore, in this paper, we propose a solution to a substantial and challenging problem in summarization, i.e., recommending a summary for a specific user. The proposed approach, called SumRecom, brings the human into the loop and focuses on three aspects: personalization, interaction, and learning user's interest without the need for reference summaries. SumRecom has two steps: i) The user preference extractor to capture users' inclination in choosing essential concepts, and ii) The summarizer to discover the user's best-fitted summary based on the given feedback. Various automatic and human evaluations on the benchmark dataset demonstrate the supremacy SumRecom in generating user-specific summaries. Document summarization and Interactive summarization and Personalized summarization and Reinforcement learning.