[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
Complimentary code for our paper HunSum-1: an Abstractive Summarization Dataset for Hungarian
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.
Automatic Meeting Summarizer - Provide Speaker wise abstractive and extractive summary of meetings, given the audio input of meeting recording.
FactSumm: Factual Consistency Scorer for Abstractive Summarization
Extractive, Abstractive text/document summarizing system with Flask, capable of suggesting topics, detecting language, summary download and text-to-speech.
Can LMs Generalize to Future Data? An Empirical Analysis on Text Summarization
This repository contains code and resources for abstractive text summarization (TS) using a novel framework that leverages knowledge-based word sense disambiguation (WSD) and semantic content generalization to enhance the performance of sequence-to-sequence (seq2seq) neural-based TS.
TF-IDF based Extractive & BERT based Abstractive text summarizer with an Interactive GUI for ease of use.
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
Interactive news summarizer system that leverages avatar narration and text to speech conversion techniques.
Codebase for the Summary Loop paper at ACL2020
Abstractive and Extractive Text summarization using Transformers.
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.
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