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
TF-IDF based Extractive & BERT based Abstractive text summarizer with an Interactive GUI for ease of use.
The goal of the project is to generate a short and concise summary that captures the salient ideas of the source text using attention model. The generated summaries potentially contain new phrases and sentences that may not appear in the source text
Complimentary code for our paper From News to Summaries: Building a Hungarian Corpus for Extractive and 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.
Tools for document summarization
Interactive news summarizer system that leverages avatar narration and text to speech conversion techniques.
Bachelor diploma thesis
Paper List (Regarding Current Projects). Mainly includes: 1) Abstractive Summarization 2) Argument Generation 3) Baseline
This is a Pytorch implementation of a summarization model that is fine-tuned on the top of Google-T5 pre-trained model.
Word-based abstractive text summarization using seq2seq-modeling with attention
Longformer Encoder Decoder model for the legal domain, trained for long document abstractive summarization task.
Abstractive text summarization models having encoder decoder architecture built using just LSTMs, Bidirectional LSTMs and Hybrid architecture and trained on TPU. Also pre-trained word embedding is used to speed up the process.
Hindi News Text Summarization
Original PyTorch implementation for TASLP 2022 Paper "SPEC: Summary Preference Decomposition for Low-Resource Abstractive Summarization."
For any given research paper , find the contributing statements present in it.
ELSA combines extractive and abstractive approaches to the automatic text summarization
Can LMs Generalize to Future Data? An Empirical Analysis on Text Summarization
Проект по курсу Физтеха "Методы оптимизации". Суть проекта заключается в исследовании методов extractive summarization.
An Efficient Memory-Enhanced Language Model for Long Document Summarization
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