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MEETING SUMMARY BART LARGE XSUM SAMSUM DIALOGSUM AMI

Developed by knkarthick
A sequence-to-sequence model based on the BART architecture, specifically fine-tuned for meeting and dialogue summarization tasks, capable of generating abstractive summaries from various dialogue data.
Downloads 119
Release Time : 3/2/2022

Model Overview

This model is fine-tuned from facebook/bart-large-xsum, specifically designed for automatic summarization of meeting minutes and dialogue texts. It combines training from multiple dialogue datasets (such as samsum, dialogsum, and the AMI meeting corpus) to generate high-quality abstractive summaries.

Model Features

Multi-dataset Fine-tuning
Trained on multiple datasets including cnndaily, newyorkdaily, xsum, samsum, dialogsum, and the AMI meeting corpus, enhancing the model's generalization capability.
Abstractive Summary Generation
Capable of understanding the core content of input text and generating new summary sentences, rather than merely extracting key fragments.
Dialogue Understanding
Specially optimized for processing dialogues and meeting records, able to capture key information in multi-party conversations.

Model Capabilities

Text Summary Generation
Dialogue Content Understanding
Meeting Minutes Compression

Use Cases

Meeting Records
Automatic Meeting Minutes Generation
Automatically compresses lengthy meeting records into concise key-point summaries
Customer Service
Customer Service Dialogue Summarization
Extracts key issues and solutions from customer service conversations
News Briefing
News Article Summarization
Generates brief summaries of news articles
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