This repository provides multiple implementations of abstractive text summarization methods, primarily targeting researchers and developers interested in NLP and sequence-to-sequence models. It offers a collection of Jupyter notebooks designed for easy execution within Google Colab, enabling users to experiment with various abstractive summarization techniques without requiring local high-performance hardware or complex setup.

How It Works

The project focuses on abstractive summarization, generating novel sentences rather than simply extracting existing ones. It implements several core architectures: a baseline seq2seq model with bidirectional LSTMs and attention, an enhanced version incorporating pointer-generator networks to mitigate common seq2seq issues, and a reinforcement learning approach for sequence-to-sequence models. These implementations are designed to be runnable in Google Colab, leveraging its free GPU resources and direct integration with Google Drive for data handling.

Quick Start & Requirements

• Installation: pip install eazymind (for a hosted API)
• Prerequisites: Google Colab environment (Python 2.7/3.x), internet connection, Google Drive integration.
• Demo: A hosted API is available at http://eazymind.herokuapp.com/arabic_sum/eazysum.
• Documentation: A series of blog posts detail the concepts and implementation steps.

Highlighted Details

• Supports multiple languages including Hindi, Amharic, English, and Arabic.
• Includes implementations based on established GitHub repositories and research papers.
• Provides a custom evaluation script (zaksum_eval.ipynb) for metrics like BLEU and ROUGE.
• Offers a hosted API (eazymind) for immediate summarization use.

Maintenance & Community

The project is maintained by Amr M. Zaki. Further details on community engagement or a roadmap are not explicitly provided in the README.

Licensing & Compatibility

The README does not explicitly state a license. The project references other repositories with varying licenses, and the use of the eazymind API may be subject to its own terms. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The project primarily targets Google Colab and uses Python 2.7 for some older implementations, which may require adaptation for modern Python environments. The hosted API requires an API key and its availability or terms of service are not detailed.