https://github.com/HLTCHKUST/QFS
공식 코드Improve Query Focused Abstractive Summarization by Incorporating Answer Relevance
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Improve Query Focused Abstractive Summarization by Incorporating Answer Relevance
Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering
FG-RAG: Enhancing Query-Focused Summarization with Context-Aware Fine-Grained Graph RAG
Unstructured Evidence Attribution for Long Context Query Focused Summarization
WebCiteS: Attributed Query-Focused Summarization on Chinese Web Search Results with Citations
Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement Learning
Generating Query Focused Summaries from Query-Free Resources
A Lightweight Constrained Generation Alternative for Query-focused Summarization
Context-aware Decoding Reduces Hallucination in Query-focused Summarization
From Local to Global: A Graph RAG Approach to Query-Focused Summarization
From Local to Global: A Graph RAG Approach to Query-Focused Summarization
From Local to Global: A Graph RAG Approach to Query-Focused Summarization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.