mlsanityData to 14 Oct 2026

ScoreRAG: A Retrieval-Augmented Generation Framework with Consistency-Relevance Scoring and Structured Summarization for News Generation

arXiv 2506.03704 ↗First posted 4 Jun 2025cs.CLcited by 1

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

This research introduces ScoreRAG, an approach to enhance the quality of automated news generation. Despite advancements in Natural Language Processing and large language models, current news generation methods often struggle with hallucinations, factual inconsistencies, and lack of domain-specific expertise when producing news articles. ScoreRAG addresses these challenges through a multi-stage framework combining retrieval-augmented generation, consistency relevance evaluation, and structured summarization. The system first retrieves relevant news documents from a vector database, maps them to complete news items, and assigns consistency relevance scores based on large language model evaluations. These documents are then reranked according to relevance, with low-quality items filtered out. The framework proceeds to generate graded summaries based on relevance scores, which guide the large language model in producing complete news articles following professional journalistic standards. Through this methodical approach, ScoreRAG aims to significantly improve the accuracy, coherence, informativeness, and professionalism of generated news articles while maintaining stability and consistency throughout the generation process. The code and demo are available at: https://github.com/peiyun2260/ScoreRAG.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 7 of its 14 references that are in the map. First posted on arXiv 4 Jun 2025.

Builds on

7 of 14 references.

References · 7

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  4. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  5. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
  6. 2024Multilingual E5 Text Embeddings: A Technical Reportcited by 372
  7. 2024Hallucination is Inevitable: An Innate Limitation of Large Language Modelscited by 332

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Budget-Constrained Online Retrieval-Augmented Generation: The Chunk-as-a-Service Modelcited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2026Coordinated Semantic Alignment and Evidence Constraints for Retrieval-Augmented Generation with Large Language Models
  2. 2025Beyond Perplexity: Let the Reader Select Retrieval Summaries via Spectrum Projection Score
  3. 2024Evaluation of Retrieval-Augmented Generation: A Survey
  4. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  5. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey
  6. 2026CHOP: Chunkwise Context-Preserving Framework for RAG on Multi Documents
  7. 2026LIT-RAGBench: Benchmarking Generator Capabilities of Large Language Models in Retrieval-Augmented Generation
  8. 2024Corrective Retrieval Augmented Generation