Textual data is a key signal for modeling relationships between news articles in recommendation systems. However, exaggerated and biased texts often hinder the accurate understanding of textual content, negatively affecting recommendation quality. Multimodal approaches have been proposed to address this issue by incorporating associated news images, but they often overlook the complementary role of visual information. To bridge this gap, we propose an Image-Guided Knowledge (IGKnow) distillation framework that transfers image-guided debiasing capability from a multimodal LLM into a text-only LLM. This design allows the distilled model to extract factual, debiased triples from text alone while preserving the benefits of multimodal learning. To further enhance stability, we introduce Triple-level Supervised Fine-Tuning (TriSFT), a permutation-invariant training for triple structures. Moreover, we refine the model through preference alignment to ensure objective, coherent knowledge extraction across news histories. We evaluate the extracted knowledge in sequential news recommendation and show that debiased triples improve recommendation performance.

Joint information extraction aims to convert unstructured text into structured knowledge by identifying entities and their relations. However, existing methods often rely on static span formation and relation-agnostic validation, limiting their ability to capture dynamic, context-sensitive semantics. We present RePooL, a hierarchical validation framework that performs fine-grained token-level filtering followed by coarse-grained span-level validation, enabling robust multi-granular semantic modeling. RePooL constructs a dual-view knowledge graph that models tokens and relations as distinct node types. It leverages auxiliary structural relations to encode token-relation semantic compatibility via subject and object roles and to compose multi-token spans dynamically, thereby enabling relation-aware validation across multiple granularities. To further strengthen semantic grounding, RePooL incorporates LLM-guided alignment, which evaluates candidate triples against the input text to specifically reinforce coherent extractions. Extensive experiments on standard IE benchmarks show that RePooL achieves superior performance, demonstrating its effectiveness in modeling fine-grained entity-relation interactions.