A simple python implementation of the Maximal Marginal Relevance (MMR) baseline system for text summarization.
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Updated
Jan 20, 2017 - Python
A simple python implementation of the Maximal Marginal Relevance (MMR) baseline system for text summarization.
[Computer Speech & Language, Elsevier] - Neural Sentence Fusion for Diversity Driven Abstractive Multi-Document Summarization.
Code for "Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning", EMNLP 2020
A multilingual and multi-document model that uses an enhanced version of TF-IDF and knowledge graphs to generate an abstractive summary
A curated list of Multi-Document Summarization papers, articles, tutorials, slides , datasets, and projects
SUPERT: Unsupervised multi-document summarization evaluation & generation
Large-scale multi-document summarization dataset and code
An Automatic Answer Summariser developed using Python, PyTorch, and HuggingFace trained on Quora Dataset aimed at summarizing and providing a single answer to a question using answers from multiple users.
Extractive Multi-document Summarization
Code for the paper: Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-Awareness
Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles
LongT5-based model pre-trained on a large amount of unlabeled Vietnamese news texts and fine-tuned with ViMS and VMDS collections
This project uses transfer learning with the T5 model to summarize multiple documents into concise summaries. It leverages advanced NLP techniques to efficiently process and extract key information from large text datasets.
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