Text Summarization
developeryj1992/ai-trend-studio
โฒ +50 โ this week
Local-first AI trend radar and live-session distillation
PythonMITupdated Nov 1, 2026
๐ฅ Trending this week in Text Summarization
Text Summarization
oomol-lab/wiki-graph
โฒ +9 โ this week
distill any book down to its spine
TypeScriptApache-2.0updated Nov 5, 2026
Text Summarization
SylphAI-Inc/AdalFlow
โฒ +8 โ this week
AdalFlow: The library to build & auto-optimize LLM applications.
๐ Search & Retrievalโ Question Answering๐ง Natural Language Processing
PythonMITupdated Nov 1, 2026
Text Summarization
codewithdark-git/DarkGPT
โฒ +7 โ this week
DarkGPT Chat Explorer is an interactive web application that allows users to engage in conversations with various GPT (Generative Pre-trained Transformer) models in real-time. This repository contains the source code for the application.
๐๏ธ Text Classification๐ Text Mining
PythonMITupdated Jul 2, 2026
Text Summarization
isas1/skills
โฒ +4 โ this week
Skills for explaining and summarising AI output: eli5-succinct, simple-summary, summary
OtherMITupdated Sep 26, 2026
Text Summarization
raiyanyahya/recall
โฒ +3 โ this week
Stop wasting tokens and re-explaining your project every session. Recall gives Claude Code , Opencode durable memory โ entirely offline.
PythonMITupdated Sep 7, 2026
Top 30 text summarization tools by stars
Text Summarization๐ฅ#1
CLUEbenchmark/CLUEDatasetSearch
ๆ็ดขๆๆไธญๆNLPๆฐๆฎ้๏ผ้ๅธธ็จ่ฑๆNLPๆฐๆฎ้
๐ Sentiment Analysis๐ท๏ธ Named Entity Recognition๐๏ธ Text Classification๐ Machine Translation๐ Search & Retrieval๐ง Natural Language Processing
Pythonno licenseupdated Nov 21, 2022
Text Summarization๐ฅ#2
SylphAI-Inc/AdalFlow
โฒ +8 โ this week
AdalFlow: The library to build & auto-optimize LLM applications.
๐ Search & Retrievalโ Question Answering๐ง Natural Language Processing
PythonMITupdated Nov 1, 2026
Text Summarization๐ฅ#3
miso-belica/sumy๐ฅ active
Module for automatic summarization of text documents and HTML pages.
๐ง Natural Language Processing
PythonApache-2.0updated Nov 11, 2026
Text Summarization#4
blmoistawinde/HarvestText
ๆๆฌๆๆๅ้ขๅค็ๅทฅๅ ท๏ผๆๆฌๆธ ๆดใๆฐ่ฏๅ็ฐใๆ ๆๅๆใๅฎไฝ่ฏๅซ้พๆฅใๅ ณ้ฎ่ฏๆฝๅใ็ฅ่ฏๆฝๅใๅฅๆณๅๆ็ญ๏ผ๏ผๆ ็็ฃๆๅผฑ็็ฃๆนๆณ
๐ Sentiment Analysis๐ท๏ธ Named Entity Recognitionโ๏ธ Information Extraction๐ง Natural Language Processing
PythonMITupdated May 13, 2024
Text Summarization#5
allenai/RL4LMs
โฒ +1 โ this week
A modular RL library to fine-tune language models to human preferences
๐ง Natural Language Processing๐ Machine Translation๐ค Language Models
PythonApache-2.0updated Mar 1, 2024
Text Summarization#6
DerwenAI/pytextrank
Python implementation of TextRank algorithms ("textgraphs") for phrase extraction
๐ง Natural Language Processing
PythonMITupdated Jun 24, 2026
Text Summarization#7
jsksxs360/How-to-use-Transformers
Transformers ๅบๅฟซ้ๅ ฅ้จๆ็จ
๐ง Natural Language Processing๐ Sentiment Analysis๐ท๏ธ Named Entity Recognition
PythonApache-2.0updated Feb 24, 2026
Text Summarization#8
dipanjanS/text-analytics-with-python
Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer.
๐ง Natural Language Processing๐ Sentiment Analysis๐๏ธ Text Classification๐ Text Mining
Jupyter NotebookApache-2.0updated Dec 24, 2020
Text Summarization#9
dmmiller612/bert-extractive-summarizer
Easy to use extractive text summarization with BERT
๐ง Natural Language Processing
PythonMITupdated Jun 12, 2023
Text Summarization#10
chiphuyen/sotawhat
Returns latest research results by crawling arxiv papers and summarizing abstracts. Helps you stay afloat with so many new papers everyday.
Pythonno licenseupdated Feb 2, 2024
Text Summarization#11
summanlp/textrank
TextRank implementation for Python 3.
๐ง Natural Language Processing
PythonMITupdated Mar 28, 2023
Text Summarization#12
JohnSnowLabs/nlu
1 line for thousands of State of The Art NLP models in hundreds of languages The fastest and most accurate way to solve text problems.
๐ Sentiment Analysis๐ท๏ธ Named Entity Recognition๐๏ธ Text Classification๐งฌ Embeddingsโ๏ธ Tokenization & Preprocessing
PythonOtherupdated Jan 28, 2025
Text Summarization#13
atulkum/pointer_summarizer
pytorch implementation of "Get To The Point: Summarization with Pointer-Generator Networks"
๐ง Natural Language Processing
PythonApache-2.0updated Jan 23, 2023
Text Summarization#14
georgian-io/LLM-Finetuning-Toolkit
โฒ +2 โ this week
Toolkit for fine-tuning, ablating and unit-testing open-source LLMs.
๐ค Language Models๐ง Natural Language Processing
PythonApache-2.0updated Oct 22, 2026
Text Summarization#15
raiyanyahya/recall
โฒ +3 โ this week
Stop wasting tokens and re-explaining your project every session. Recall gives Claude Code , Opencode durable memory โ entirely offline.
PythonMITupdated Sep 7, 2026
Text Summarization#16
opea-project/GenAIExamples
Generative AI Examples is a collection of GenAI examples such as ChatQnA, Copilot, which illustrate the pipeline capabilities of the Open Platform for Enterprise AI (OPEA) project.
๐ Search & Retrieval
ShellApache-2.0updated Nov 3, 2026
Text Summarization#17
kubestellar/kubestellar๐ฅ active
โฒ +2 โ this week
KubeStellar - a flexible solution for multi-cluster configuration management for edge, multi-cloud, and hybrid cloud
GoApache-2.0updated Nov 9, 2026
Text Summarization#18
RecapAI/Recap
โฒ +2 โ this week
Open Source, Privacy-First, macOS-Native AI Meeting Summary
SwiftMITupdated Feb 7, 2026
Text Summarization#19
yongzhuo/Macropodus
โฒ +1 โ this week
่ช็ถ่ฏญ่จๅค็ๅทฅๅ ทMacropodus๏ผๅบไบAlbert+BiLSTM+CRFๆทฑๅบฆๅญฆไน ็ฝ็ปๆถๆ๏ผไธญๆๅ่ฏ๏ผ่ฏๆงๆ ๆณจ๏ผๅฝๅๅฎไฝ่ฏๅซ๏ผๆฐ่ฏๅ็ฐ๏ผๅ ณ้ฎ่ฏ๏ผๆๆฌๆ่ฆ๏ผๆๆฌ็ธไผผๅบฆ๏ผ็งๅญฆ่ฎก็ฎๅจ๏ผไธญๆๆฐๅญ้ฟๆไผฏๆฐๅญ(็ฝ้ฉฌๆฐๅญ)่ฝฌๆข๏ผไธญๆ็น็ฎ่ฝฌๆข๏ผๆผ้ณ่ฝฌๆขใtookit(tool) of NLP๏ผCWS(chinese word segnment)๏ผPOS(Part-Of-Speech Tagging)๏ผNER(name entity recognition)๏ผFind(new words discovery)๏ผKeyword(keyword extraction)๏ผSummarize(text summarization)๏ผSim(text similarity)๏ผCalculate(scientific calculator)๏ผChi2num(chinese number to arabic number)
๐ท๏ธ Named Entity Recognition๐ง Natural Language Processing
PythonMITupdated Mar 24, 2023
Text Summarization#20
dongjun-Lee/text-summarization-tensorflow
Tensorflow seq2seq Implementation of Text Summarization.
PythonMITupdated Nov 20, 2022
Text Summarization#21
chakki-works/sumeval
Well tested & Multi-language evaluation framework for text summarization.
PythonApache-2.0updated Apr 13, 2026
Text Summarization#22
cognitivetech/ollama-ebook-summary
โฒ +1 โ this week
LLM for Long Text Summary (Comprehensive Bulleted Notes)
Pythonno licenseupdated Jul 5, 2025
Text Summarization#23
ChenRocks/fast_abs_rl
Code for ACL 2018 paper: "Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting. Chen and Bansal"
๐ง Natural Language Processing
PythonMITupdated Jul 23, 2020
Text Summarization#24
Text Summarization#25
namuan/dr-doc-search
โฒ +1 โ this week
Converse with book - Built with GPT-3
PythonMITupdated Oct 1, 2024
Text Summarization#26
awslabs/generative-ai-cdk-constructs๐ฅ active
AWS Generative AI CDK Constructs are sample implementations of AWS CDK for common generative AI patterns.
๐ Search & Retrieval
TypeScriptApache-2.0updated Nov 12, 2026
Text Summarization#27
theamrzaki/text_summurization_abstractive_methods
โฒ +1 โ this week
Multiple implementations for abstractive text summurization , using google colab
๐งฌ Embeddings๐ง Natural Language Processing
Jupyter Notebookno licenseupdated Oct 6, 2020
Text Summarization#28
udibr/headlines
Automatically generate headlines to short articles
๐ง Natural Language Processing
Jupyter NotebookMITupdated Apr 22, 2018
Text Summarization#29
Text Summarization#30
HHousen/TransformerSum
Models to perform neural summarization (extractive and abstractive) using machine learning transformers and a tool to convert abstractive summarization datasets to the extractive task.
๐ง Natural Language Processing
PythonGPL-3.0updated May 26, 2025
๐ New research on Text Summarization
๐ arXiv:2611.14364 ยท cs.CL46h ago
ARE-bench: Benchmarking Agents' Research Ability in Real-World Researcher Sessions and Workspaces
Jiahang Lin, Wanying Ren, Wanghan Xu +24
Research agents achieve increasingly strong benchmark scores, yet these scores do not necessarily translate into reliable delivery in ongoing research projects. Many research-agent benchmarks emphasize standalone tasks and relatively self-contained workflows in clean or curated workspaces. However, researchers ask agents to complete one step within an ongoing project, where requests depend on earlier session decisions and workspaces contain unfinished files, outdated versions, and failed attempts. We introduce ARE-bench to evaluate whether research agents advance ongoing projects while respecting prior decisions and existing workspace state, grounded in real session context and in-progress workspaces. ARE-bench includes 75 tasks built on agent sessions and full workspaces from recruited researchers' own projects, spanning seven research stages, 33 requirements, and 150 sub-requirements, with 1,173 binary checklist items in total. Each task is anchored at a real user request, preserves its requirements, and summarizes the earlier requests still in effect as background. The complete workspace is replayed to that boundary without cleanup, preserving artifacts from earlier turns. Each checklist is compiled from an intent-aligned rubric and credits any valid solution. Across seven frontier models, the strongest passes every checklist item on only 66.2% of tasks, and Design and Execution rank among the hardest stages for every model. Three models pass about 69% of tasks when only delivery contracts and general norms are checked, but just 28.4% to 40.0% once historical and material or workspace requirements are also checked. Evaluating delivery without these contextual requirements can therefore overestimate research agents and mask differences between them.โผ more
๐ Text Summarization
๐ arXiv:2611.13914 ยท cs.AI2d ago
From Counterfactual Edits to Human Narratives: LLM-Driven Interpretations for Recommender System Explanations
Vojtech Cahlik, Amir Reza Mohammadi, Rodrigo Alves +2
๐ฌ Published at CIKM 2026 (Rome, Italy). Code: https://github.com/vcahlik/from-counterfactual-edits-to-human-narratives
Recommender systems increasingly influence what users discover and consume online, yet the decision logic behind their outputs often remains opaque. Counterfactual explanation (CFX) methods provide an effective way to show which changes to a user's interaction history would alter a recommendation. In recommendation settings, however, CFXs often require many edits, producing long, low-level lists that are hard for users to interpret. Moreover, most evaluations emphasize edit counts while largely overlooking whether the resulting CFXs are coherent and actionable for users. We argue that this gap between generating valid CFXs and communicating model behavior stems from the lack of user-centered abstraction and limited qualitative evaluation. To bridge this gap, we investigate large language models as mediators that summarize raw CFX interaction edit lists into concise natural language explanations describing higher-level preference patterns. We optimize these mediators for correctness, informativeness, and linguistic quality through preference optimization, and evaluate the resulting explanations with human-calibrated metrics derived from separate human-labeled datasets. On the MovieLens-1M dataset, across multiple counterfactual generators and language models, the resulting summaries achieve high overall linguistic quality, while the strongest semantic results are obtained with high-success CFX methods such as ACCENT and SPINRec. Direct Preference Optimization improves the semantic metrics in almost all evaluated settings.โผ more
๐ Text Summarization๐ Search & Retrieval๐ค Language Models
๐ arXiv:2611.12725 ยท cs.LG3d ago
Scaling Taxonomy-Free Image Labeling with Generative AI: A Multimodal
Zhenjie Zhang, Sainan Chen, Kshiteesh Hegde +5
๐ฌ 5 pages, 3 figures, 4th CJ Workshop at KDD 2025 (https://sites.google.com/view/kddcjworkshop2025/accepted-papers), also related: https://arxiv-org.atlassian.net/servicedesk/customer/portal/2/MOD-93955
Providing real-time multimodal insights into images is challenging because it requires low latency, high description accuracy, and scalable solutions that are both cost-effective and efficient. For example, given a new image of an outfit, we would like to generate insights that the image depicts "earth tones" and "linen pants" and allow users to explore similar ideas along these themes. Traditional strategies to generate such insights would typically involve building taxonomies producing a golden set labeled by humans, training models on these datasets, and deploying these models for online inference services. Such workflows are constrained by fixed taxonomy, high labeling cost, limited labeling coverage in sample space, and human errors and expertise gaps in labelers. In addition, these methods are expensive and cumbersome to iterate over time. Recent trends have started exploring generative AI models for online insights, but this method suffers from long latency and prohibitive costs to support a high volume of requests or large amounts of offline labeling needs. We present Generative Labeling On Clusters (GLOC) as a solution to provide highly scalable, cost-effective, low latency, and high accuracy online image insight services. GLOC leverages highly optimized image embeddings to form image clusters that are semantically close to each other for the purpose of the type of insights, and summarizes the insights at the cluster level, which effectively generalizes to members in the cluster. At Pinterest, GLOC powers image descriptors and pivot query suggestions for Hybrid Search, which is a multimodal search service enabling users to search with both an image and a text query. Our offline and online A/B tests demonstrate that GLOC significantly outperforms previous methods, delivering improvements in quality and efficiency.โผ more
๐ Text Summarization๐งฌ Embeddings
๐ arXiv:2611.11902 ยท cs.LG3d ago
Spectral Unlearning: Transformer Structure-Preserving Updates for Language Models
Sung Il Choi, Junhao Cai, Dohun Kim +1
๐ฌ Accepted at NeurIPS 2026
Machine unlearning in large language models (LLMs) aims to remove specific learned knowledge, capabilities, or behaviors while preserving model utility. Existing methods have mostly focused on the design of unlearning objectives, while largely overlooking the intrinsic matrix structure of transformer weights. In particular, standard optimizers operate on independent scalar coordinates, thereby ignoring this matrix structure. We propose spectral unlearning, an update framework that combines standard unlearning objectives with matrix-structured optimization. We show that the spectral update corresponds to the steepest-descent direction for transformers composed of linear layers with normalized inputs and outputs. Our approach is applied on top of existing unlearning methods and evaluated across complementary axes of memorization, privacy, and utility, summarized via their harmonic mean. Spectral unlearning consistently outperforms a broad range of baselines and closes most of the gap to a retain-trained reference. Code is available at https://github.com/unlearn-ml/spectral-unlearning/.โผ more
๐ Text Summarization๐ค Language Models