cliff_summ
Contrastive Summarization Framework
Provides a framework for improving the faithfulness and factuality of abstractive summarization models through contrastive learning
Code for EMNLP 2021 paper "CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization"
45 stars
1 watching
8 forks
Language: Python
last commit: about 3 years ago Related projects:
Repository | Description | Stars |
---|---|---|
| Provides a pre-trained text summarization model that can be deployed as a web service in a Docker container | 27 |
| Provides Ruby bindings to an open text summarizer library. | 43 |
| A deep learning framework for contrastive learning from unpaired medical images and texts | 473 |
| Provides explanations for why an instance has a certain outcome by contrasting it with what would have happened if the outcome had been different. | 45 |
| A Ruby wrapper for the Open Text Summarizer library to summarize text content. | 204 |
| An unsupervised contrastive learning framework for learning sentence embeddings sensitive to differences between original and edited sentences. | 292 |
| An implementation of a dense video captioning model with attention-based fusion and context gating | 149 |
| A framework for collaborative machine learning model training that leverages similarity between model representations to correct local training. | 267 |
| A PyTorch-based framework for 3D scene understanding with holistic context graph and relation-based optimization in panoramic images | 90 |
| Develops algorithms to restore sharp images from blurry ones and interpolate missing frames in video sequences with improved accuracy | 81 |
| An Elixir library that extracts and curates primary readable content from web pages. | 260 |
| A language that facilitates designing machine learning models with flexible configurations | 64 |
| An unsupervised machine learning algorithm that extracts meaningful patterns from data using contrastive predictive coding | 527 |
| Provides a benchmark dataset and tools for training text summarization models in the Indonesian language. | 77 |
| An end-to-end image captioning system that uses large multi-modal models and provides tools for training, inference, and demo usage. | 1,849 |