A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Xinze Li, Yixin Cao, Yubo Ma, et al.
This paper compares Long Context and RAG for LLMs, finding LC generally outperforms RAG on QA benchmarks, with summarization-based retrieval matching LC, and highlights context relevance as an overlooked factor.
Haopeng Zhang, Philip S. Yu, Jiawei Zhang
A comprehensive survey of text summarization covering statistical methods, deep learning, PLMs, and LLMs, including datasets, metrics, and future directions.
Jiangnan Fang, Cheng-Tse Liu, Jieun Kim, et al.
A multi-LLM framework for text summarization using centralized and decentralized evaluation strategies.
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Proposes TLDR, a method for extreme summarization of scientific papers into single-sentence summaries of key contributions.
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PEGASUS introduces a self-supervised pre-training objective that masks important sentences in documents and generates them as a summary, improving abstractive summarization.
Yubin Hong, Chaofan Li, Jingyi Zhang, et al.
FG-RAG introduces a context-aware fine-grained graph retrieval-augmented generation framework to improve query-focused summarization.
Darren Edge, Ha Trinh, Newman Cheng, et al.
This paper introduces Graph RAG, a method that uses knowledge graphs to improve query-focused summarization by enhancing comprehensiveness and diversity over naive RAG.