summarization

This article explains how compaction works in the Pi coding agent, summarizing old conversation history when the context window nears its limit, and details Pi's specific implementation and triggers.

Kin Health is a product that records doctor visits and provides clear summaries for patients.

This paper presents a grounded and decomposed framework for evaluating relation-level hallucinations in abstractive summarization, introducing a normalized Relation Hallucination Index (RHI) with linguistically informed relation extraction enhancements.

Wired highlights Meetily, a free open-source app that transcribes and summarizes meetings locally without subscription fees or cloud uploads, working across major video conferencing platforms.

This paper proposes MIDAS, a multi-LLM framework for data-adaptive summarization that automates prompt optimization for domain-specific enterprise use cases, achieving strong improvements over prior methods on customer ticket summarization benchmarks.

This paper from Stony Brook University identifies 'Averaging Bias' in human faithfulness annotations for text summarization: global human labels correlate better with the average of per-sentence LLM judgments than with a strict conjunctive rule, meaning humans often label summaries as faithful even when they contain local factual errors.

YouTube Summarizer is an open-source Flask web app that summarizes YouTube videos and playlists using Google Gemini or OpenAI models, with caching, audio output, and Docker support.

This technical report introduces Naver-News-KO, a Korean news summarization dataset of 27,400 (document, summary) pairs collected from Naver News, hosted on Hugging Face and used for fine-tuning open-source summarization models like Gemma-2B-ko and KoBART.

Boris Cherny released a 7-page PDF on Graph Engineering, showing how to use four Claude prompts to replace trained ML models by building a persistent knowledge graph for agents, with steps for extraction, resolution, summarization, and querying.

This paper proposes a generative AI-assisted summarization framework using GPT-5 model variants to address input-length limitations in automated essay scoring, demonstrating trade-offs between model capacity, summary fidelity, and computational cost on the ASAP 2.0 dataset.

Proposes Token-Level Off-Policy Labeling (TOPL), an off-policy training paradigm for faithful generation that reframes post-training as token-level correctness prediction, achieving strong out-of-distribution generalization across summarization and machine translation tasks.

The author criticizes the RAG and vector database approach, proposing that the correct methods include using memory correctly, chunking and indexing, summarizing, providing search tools for agents, and using SRAM-only inference services such as Groq and Cerebras.

An AI-powered tool for extracting knowledge and generating summaries from PDF books page by page.

The article discusses how AI agent performance degrades over long sessions due to context window clutter from raw history, tool outputs, and repeated reasoning, and suggests solutions like summarizing old turns and trimming tool outputs to extend useful run length.

A Python script that can automatically parse PDF book content, extract key knowledge points, and generate Markdown-format summaries, aiming to improve reading and knowledge organization efficiency.

Recall is an open-source tool that provides fully-local, zero-cost project memory for Claude Code by automatically capturing session history and summarizing it into a compact context file, all without sending data to any external API.

This paper proposes Detect–Remask–Repair, a diffusion-based framework for localized faithfulness repair in summarization when contexts evolve, and introduces the StreamSum benchmark for evaluating such settings. Experiments show it offers controllable trade-offs between faithfulness, speed, and content preservation.

This paper evaluates context engineering configurations for LLM agents in enterprise tool-use workflows, showing that summarization with selective pruning achieves 91.6% accuracy while reducing token usage by over 60% compared to full-context baselines.

A comparison of LLM summarization performance shows Qwen 3 leads the 30B parameter range, followed by Gemma 4, while newer Qwen models may be optimized for agentic tasks.

ColibotAI is an on-device AI tool that translates, summarizes, and explains any text without needing internet connection.