This is the second post in a two-part series in which I propose a practical guide for organizations so you can assess the quality of text summarization models for your domain.
For an introduction to text summarization, an overview of this tutorial, and the steps to create a baseline for our project (also referred to as section 1), refer back to the first post.
This post is divided into three sections:

Section 2: Generate summaries with a zero-shot model
Section 3: Train a summarization model
Section 4: Evaluate the trained model

Section 2: Generate summaries with a zero-shot model
In this post, we use the concept of zero-shot learning (ZSL), which means we use a model that has been trained to summarize text but hasn’t seen any examples of the arXiv dataset. It’s a bit like trying to paint a portrait when all you have

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