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
LongT5-based model pre-trained on a large amount of unlabeled Vietnamese news texts and fine-tuned with ViMS and VMDS collections
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
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