Scalable Machine Learning Process for Abstractive Text Summarization in German and English with Google-T5
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Updated
Dec 2, 2021 - Python
Scalable Machine Learning Process for Abstractive Text Summarization in German and English with Google-T5
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).
Deep Reinforced Model for Abstractive Summarization
Modified Code for ACL 2018 paper by Chen and Bansal for Query Focused 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.
This is a Pytorch implementation of a summarization model that is fine-tuned on the top of Google-T5 pre-trained model.
AACL'2022: Unsupervised Single Document Abstractive Summarization using Semantic Units
[DATA22 and Springer LNCS] Graph-Enhanced Biomedical Abstractive Summarization via Factual Evidence Extraction
[Computer Speech & Language, Elsevier] - Neural Sentence Fusion for Diversity Driven Abstractive Multi-Document Summarization.
Original PyTorch implementation for TASLP 2022 Paper "SPEC: Summary Preference Decomposition for Low-Resource Abstractive Summarization."
ELSA combines extractive and abstractive approaches to the automatic text summarization
Code for Master's Thesis on 'Neural Automatic Summarization' written at the IT University of Copenhagen
Генерация новостных заголовков
Automatic text summarization with a pre-trained encoder and a transformer decoder (BERT). Provides a web interface for the models using Django
[EACL 2021] - Unsupervised Abstractive Summarization of Bengali Text Documents.
Implementation of paper: "A Neural Attention Model for Sentence Summarization" in Theano
Text Summarization with Pretrained Encoders
Code for our work "Read, Highlight and Summarize: A Hierarchical Neural Semantic Encoder-based Approach"
Code and data for the Dreyer et al (2023) paper on abstractiveness and factuality in abstractive summarization
Project on Abstractive Summarization of News for course on Natural Language Processing at IIT Delhi
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