Update README.md

README.md

@@ -20,7 +20,7 @@ pipeline_tag: summarization
 ![Bart Logo](https://huggingface.co/front/assets/huggingface_logo.svg)
 
 This repository contains the **Bart-Large-paper2slides-summarizer Model**, which has been fine-tuned on the [Automatic Slide Generation from Scientific Papers dataset](https://www.kaggle.com/datasets/andrewmvd/automatic-slide-generation-from-scientific-papers) using unsupervised learning techniques using an algorithm from the paper entitled '[Unsupervised Machine Translation Using Monolingual Corpora Only](https://arxiv.org/abs/1711.00043)'.
-Its primary focus is to summarize **scientific texts** with precision and accuracy, the model is parallelly trained with another model from the same contributor.
+Its primary focus is to summarize **scientific texts** with precision and accuracy, the model is parallelly trained with the [**Bart-large-paper2slides-expander**](https://huggingface.co/com3dian/Bart-large-paper2slides-expander) from the same contributor.
 
 ## Model Details
 
@@ -67,7 +67,7 @@ pip install transformers
 
 ## Model Fine-tuning Details
 
-The fine-tuning process for this model involved training on the slide generation dataset using unsupervised learning techniques. Unsupervised learning refers to training a model without explicit human-labeled targets. Instead, the model learns to generate slides by maximizing the likelihood of generating the correct output given the input data.
+The fine-tuning process for this model involved training on the slide generation dataset using unsupervised learning techniques. Unsupervised learning refers to training a model without explicit human-labeled targets. Instead, the model learns to back-summarize the input provided by the expansion model, into the original texts.
 
 The specific hyperparameters and training details used for fine-tuning this model are as follows: