Deep Reinforcement Learning For Sequence to Sequence Models
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Updated
Mar 24, 2023 - Python
Deep Reinforcement Learning For Sequence to Sequence Models
Abstractive summarisation using Bert as encoder and Transformer Decoder
This repository contains the code, data, and models of the paper titled "XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages" published in Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021.
ACL 2020 Unsupervised Opinion Summarization as Copycat-Review Generation
A tool to automatically summarize documents abstractively using the BART or PreSumm Machine Learning Model.
[AAAI2021] Unsupervised Opinion Summarization with Content Planning
Gazeta: Dataset for automatic summarization of Russian news / Газета: набор данных для автоматического реферирования на русском языке
[ACL-IJCNLP 2021] Self-Supervised Multimodal Opinion Summarization
[ACL2020] Unsupervised Opinion Summarization with Noising and Denoising
An optimized Transformer based abstractive summarization model with Tensorflow
non-anonymized cnn/dailymail dataset for text summarization
Abstractive Summarization in the Nepali language
Abstractiv Text Summarization
An ai-as-a-service for abstractive text summarizaion
Summarizing text to extract key ideas and arguments
Using a deep learning model that takes advantage of LSTM and a custom Attention layer, we create an algorithm that is able to train on reviews and existent summaries to churn out and generate brand new summaries of its own.
perfroming abstractive text summariztion task using T5, and serving it via REST API
[Computer Speech & Language, Elsevier] - Neural Sentence Fusion for Diversity Driven Abstractive Multi-Document Summarization.
It is the edited version of the PreSumm model. You can easily follow the instructions to train an Abstractive Text Summarizer model (which was challenging in the original codes).
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