Panoramic Interests: Stylistic-Content Aware Personalized Headline Generation
Junhong Lian, Xiang Ao, Xinyu Liu, Yang Liu, Qing He

TL;DR
This paper introduces SCAPE, a novel framework for personalized news headline generation that incorporates both content and stylistic preferences using large language models and contrastive learning, leading to improved personalization.
Contribution
SCAPE is the first to integrate stylistic and content preferences for personalized headlines through a hierarchical fusion network with LLM collaboration.
Findings
SCAPE outperforms baseline methods on PENS dataset.
Incorporating stylistic preferences enhances headline personalization.
Contrastive learning improves interest modeling accuracy.
Abstract
Personalized news headline generation aims to provide users with attention-grabbing headlines that are tailored to their preferences. Prevailing methods focus on user-oriented content preferences, but most of them overlook the fact that diverse stylistic preferences are integral to users' panoramic interests, leading to suboptimal personalization. In view of this, we propose a novel Stylistic-Content Aware Personalized Headline Generation (SCAPE) framework. SCAPE extracts both content and stylistic features from headlines with the aid of large language model (LLM) collaboration. It further adaptively integrates users' long- and short-term interests through a contrastive learning-based hierarchical fusion network. By incorporating the panoramic interests into the headline generator, SCAPE reflects users' stylistic-content preferences during the generation process. Extensive experiments…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDigital Humanities and Scholarship · Semantic Web and Ontologies · Video Analysis and Summarization
MethodsAttentive Walk-Aggregating Graph Neural Network · Focus · Linear Layer · Absolute Position Encodings · Position-Wise Feed-Forward Layer · Residual Connection · Label Smoothing · Dropout · Byte Pair Encoding · Adam
