Resource list for text summarization approaches
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This repository serves as a comprehensive guide to text summarization, covering fundamental concepts, various approaches (extractive, abstractive, and hybrid), and essential resources for researchers and practitioners. It aims to demystify the field by providing a structured overview of techniques, evaluation metrics, datasets, and relevant libraries.
How It Works
The guide categorizes summarization into extractive (selecting key sentences) and abstractive (generating novel sentences). Extractive methods include graph-based (e.g., TextRank, LexRank), feature-based, topic-based (LSA), grammar-based, and neural network approaches. Abstractive methods primarily leverage encoder-decoder architectures, often enhanced with attention mechanisms and pointer networks to handle novel words and long documents. Hybrid approaches combine extractive and abstractive techniques for improved fluency and accuracy.
Quick Start & Requirements
This is a curated list of resources, not a runnable library. To implement summarization techniques, users will need to consult the linked libraries and papers.
gensim
(for TextRank, LSA), pytextrank
, TextTeaser
, TensorFlow
, sumeval
.Highlighted Details
Maintenance & Community
This repository is a curated list of resources and does not appear to have active development or a dedicated community forum. It is a static guide.
Licensing & Compatibility
The repository itself is a collection of links and information; it does not have a specific license. The underlying libraries and datasets mentioned will have their own licenses, which users must consult.
Limitations & Caveats
This is a guide and not an executable library, requiring users to integrate various tools and models themselves. The field of text summarization is rapidly evolving, and this guide may not reflect the absolute latest advancements or state-of-the-art models.
2 years ago
Inactive