A Abstractive Summarization Implementation with Transformer and Pointer-generator

:scorpius::heavy_plus_sign::sagittarius::arrow_right::heavy_check_mark: Build a summarizer models combining transformers and pointing mechanism

Pointer-generator transformer model and transformer model for the morphological inflection task. custom to the SIGMORPHON 2019 shared task.

Transformer with pointer generator for machine translation

An implementation of pointer generator network by using bert as encoder and transformer decoder.

An Abstractive Summarization(for Datasets in English format) Implementation with Transformer and Pointer-generator

Summarizes documents using RNN,Transformer(based on pointer generator network)

Examples of models for Text Summarization using AWS SageMaker and W&B. Using Bi-LSTMs, Attention, Pointer Generator, Transformers, T5 and transformer library

Code implementation for the Paper "Making a Point: Pointer-Generator Transformers for Disjoint Vocabularies" (https://www.aclweb.org/anthology/2020.aacl-srw.13.pdf).

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Transformer and pointer-generator transformer models for morphological inflection. Submission of the NYU-CUBoulder team to the SIGMORPHON 2020 shared task.

Get to the Point

Pointer generator transformer model for summarization

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Implementation of summarization using transformers and hybrid pointer generator networks

A hybrid code-switched text generator model based on pointer generator network and transformer

A PyTorch framework implementing and comparing advanced abstractive text summarization architectures (Transformer, Pointer-Generator, Neural Intra-Attention) from scratch.

My Implementation of an enhanced encoder-decoder Transformer. Combines the original Attention Is All You Need architecture with RAT-SQL-style relation-aware encoding and a pointer-generator decoder to turn natural-language questions into accurate SQL queries.

Rare-Word Retention in Abstractive Summarisation via Hybrid Copy-Aware Transformer. This project explores the integration of pointer–generator and coverage mechanisms into BART-base, combined with span-aware decoding and entity-focused evaluation, to improve factuality and rare-word/entity retention in CNN/DailyMail summarisation.