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This paper introduces a method to improve factual consistency in text summarization by aggregating scores from multiple weak metrics via preference learning, achieving consistent factuality gains across various language models.
MemFail is a diagnostic benchmark that isolates failure modes of LLM memory systems by formalizing summarization, storage, and retrieval operations, and evaluating them with adversarially designed datasets.
This paper investigates the risk of sensitive information inference from exported LLM representations in clinical summarization, showing that reducing leakage from one vector artifact does not guarantee privacy in others. It introduces SurfaceLoRA, a fine-tuning method that reduces race recovery from targeted vectors while preserving utility.
Introduces parallel context compaction for long-horizon LLM agents, enabling fine-grained control over summary volume and reducing end-to-end latency compared to sequential synchronous compaction across multiple backbone models.
A Meta paper shows that coding agents improve significantly when they reuse short summaries of past attempts instead of raw logs, achieving strong gains on SWE-Bench and Terminal-Bench with Claude 4.5 Opus.
Meetily is a privacy-first, open-source AI meeting assistant that captures, transcribes, and summarizes meetings entirely locally on the user's infrastructure.
This paper proposes an evidence-based model to automatically generate query keywords from query-free summarization datasets, enabling the creation of query-focused summarization datasets. Experimental results show that summaries generated using evidence-based queries achieve competitive ROUGE scores compared to original queries.
SCURank introduces Summary Content Units to rank candidate summaries, enabling small models distilled from multiple LLMs to outperform traditional metrics and single-LLM distillates.
A novice asks for recommendations on small language models and prompting strategies to build an employee note summarization engine under 2000 tokens, after experiencing hallucinations with Qwen2.5-7B-Instruct.
This paper introduces the one-sided conversation problem (1SC), addressing how to reconstruct missing dialogue and generate summaries when only one speaker's turns are available in real-world settings like telemedicine and call centers. The authors evaluate prompting and finetuned models on multiple datasets, finding that access to future context and utterance length information improves reconstruction, while high-quality summaries can be generated without full dialogue reconstruction.
AICW Summarize Widget lets website visitors summarize page content using their preferred AI tool. It is a simple embeddable widget aimed at improving content accessibility.
OpenAI presents a scalable alignment technique using hierarchical summarization of entire books with human feedback, demonstrating how models can be trained to act in accordance with human intentions on complex, difficult-to-evaluate tasks.
OpenAI demonstrates a technique for improving language model summarization by training a reward model on human preferences and fine-tuning models with reinforcement learning, achieving significant quality improvements that generalize across datasets. This work advances model alignment through human feedback at scale, with applications beyond summarization.
OpenAI demonstrates fine-tuning GPT-2 (774M parameters) using human preference feedback for text continuation and summarization tasks, requiring 5k labels for stylistic tasks and 60k for summarization, with models achieving 86-88% human preference rates though revealing labeler heuristic exploitation.
Meetily is an open-source, privacy-first AI meeting assistant that runs locally to capture, transcribe, and summarize meetings without sending data to the cloud. It includes a PRO version with advanced features like enhanced accuracy and custom workflows.
An AI agent skill that researches any topic across multiple platforms including Reddit, X, YouTube, Hacker News, Polymarket, and the web, then synthesizes a grounded summary of the last 30 days of activity.
NVIDIA releases a reference blueprint for building vision agents and AI-powered video analytics applications, including real-time intelligence, downstream analytics, and agentic workflows for search, summarization, and Q&A.