[ACL-IJCNLP 2021] Self-Supervised Multimodal Opinion Summarization
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Updated
Apr 6, 2024 - Python
[ACL-IJCNLP 2021] Self-Supervised Multimodal Opinion Summarization
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.
FactSumm: Factual Consistency Scorer for Abstractive Summarization
Implementation of the paper "FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations (NAACL 2022)"
Automatic text summarization with a pre-trained encoder and a transformer decoder (BERT). Provides a web interface for the models using Django
Code and data for the Dreyer et al (2023) paper on abstractiveness and factuality in abstractive summarization
ACL 2020 Unsupervised Opinion Summarization as Copycat-Review Generation
Topic-Aware Convolutional Neural Networks for Extreme Summarization
Codebase for the Summary Loop paper at ACL2020
Abstractive summarisation using Bert as encoder and Transformer Decoder
Original PyTorch implementation for TASLP 2022 Paper "SPEC: Summary Preference Decomposition for Low-Resource Abstractive Summarization."
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.
Original PyTorch implementation for AAAI 2021 Paper "Meta-Transfer Learning for Low-Resrouce Abstractive Summarization."
Project on Abstractive Summarization of News for course on Natural Language Processing at IIT Delhi
AACL'2022: Unsupervised Single Document Abstractive Summarization using Semantic Units
Deep Reinforced Model for Abstractive Summarization
Генерация новостных заголовков
[DATA22 and Springer LNCS] Graph-Enhanced Biomedical Abstractive Summarization via Factual Evidence Extraction
An optimized Transformer based abstractive summarization model with Tensorflow
Abstractive Multi-Document Summarisation, generating Wikipedia lead sections for specific domains. Exploiting target summaries content structure. Specific categories from WikiSum dataset.
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