This repository serves as a curated collection of resources for text summarization, targeting researchers and practitioners in NLP. It aims to provide a comprehensive guide to the field, covering key research topics, essential papers, available models, and datasets, thereby facilitating learning and development in text summarization.

How It Works

The repository organizes information into logical sections, starting with fundamental definitions and task categories (extractive vs. abstractive summarization). It then delves into main research topics like multi-document and long-document summarization, performance improvements through transfer learning and knowledge enhancement, and post-editing techniques. The project also addresses challenges such as data scarcity and evaluation metrics, while exploring controllable text generation and aspect-based summarization.

Quick Start & Requirements

• Installation: No direct installation instructions are provided as this is a resource repository. However, links to code repositories for specific models (e.g., KoBART, KoBertSum) are available.
• Prerequisites: Familiarity with NLP concepts, embeddings, transfer learning, and Transformer/BERT architectures is recommended for deeper engagement.
• Resources: Links to various datasets (e.g., AIHub, WikiLingua, MLSUM) and pre-trained models (e.g., BERT, KoBART, KcBERT) are provided.

Highlighted Details

• Comprehensive categorization of summarization tasks and main research topics.
• Detailed lists of "Must-read Papers" with keywords and brief descriptions, spanning from classic methods like TextRank to modern approaches like BART and PEGASUS.
• Extensive compilation of Korean and English datasets, including details on domain, length, volume, and licensing.
• A thorough list of pre-trained models, with a focus on Korean language models.

Maintenance & Community

• The repository is maintained by uoneway.
• Links to related resources and other "awesome" lists are provided for further exploration.

Licensing & Compatibility

• Licenses vary by dataset and model. Some datasets are available under CC-BY-SA-4.0, MIT, or non-commercial research purposes only. Pre-trained models also have different licenses (e.g., Apache 2.0, MIT).

Limitations & Caveats

• This repository is a curated list of resources and does not provide a unified framework or tool for text summarization. Users will need to refer to individual model repositories for implementation and usage details.