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README.md

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 # TL;DR
 
-Scientific Abstract Simplification (SAS) is a tool that rewrites difficult-to-understand scientific abstracts into simpler, easier-to-read versions. 
-Our goal is to make scientific knowledge more accessible to everyone. If you've already tried our baseline model (`sas_baseline`), 
-the current model is even better on all evaluation metrics. You can try it now with the Hosted Inference API on the right. 
-Simply choose one of the provided examples or enter your own scientific abstract. 
-Just remember to include the instruction "summarize, simplify, and contextualize: " at the beginning (with a space after the colon). 
-For local use, see the [Usage](#Usage) section.
+Scientific Abstract Simplification (SAS) is a tool designed to rewrite complex scientific abstracts into simpler, more comprehensible versions. Our objective is to make scientific knowledge universally accessible. If you have already experimented with our baseline model (`sas_baseline`), you will find that the current model surpasses its predecessor in terms of all evaluation metrics. Feel free to test it via the Hosted Inference API to your right. Simply select one of the provided examples or input your own scientific abstract. Just ensure to precede your text with the instruction, "summarize, simplify, and contextualize: ", followed by a space. For local usage, refer to the [Usage](#Usage) section."
 
 # Project Description
 
 
-Open science has greatly reduced the barriers to accessing scientific papers. However, reachable research does not mean accessible knowledge. 
-As a result, many people may prefer to trust short stories on social media rather than attempting to read a scientific paper. 
-This is understandable, as we humans often prefer stories to dry, technical information.
-So, why not "translate" these complex scientific abstracts into simpler, more accessible stories? Some prestigious journals are already taking steps towards greater accessibility. 
-For example, PNAS requires authors to submit Significance Statements that can be understood by an "undergraduate-educated scientist," and Science includes an editor abstract for a quick overview of the paper's key points.
-
-In this project, we aim to use AI to rewrite scientific abstracts as easily understandable scientific stories. 
-To do this, we have created two new datasets: one containing PNAS abstract-significance pairs, and the other containing editor abstracts from Science. 
-We use a Transformer model (a variant called Flan-T5) to fine-tune our model for the task of simplifying scientific abstracts. Initially, the model will be fine-tuned using multiple discrete instructions by combining four relevant tasks in a challenge-proportional manner (i.e., we call it Multi-Instruction Pretuning). 
-Then, we continue fine-tuning the model using only the abstract-significance corpus. Our model is able to produce lay summaries that are better than models fine-tuned only with the abstract-significance corpus and models fine-tuned with task combinations in traditional ways. 
-We hope our work can facilitate greater understanding of scientific research and allow more people to benefit from open science.
+Open science has significantly reduced barriers to accessing scientific papers.
+However, attainable research does not entail accessible knowledge.
+Consequently, many individuals might prefer to rely on succinct social media narratives rather than endeavour to comprehend a scientific paper.
+This preference is understandable as humans often favor narratives over dry, technical information. 
+So, why not "translate" these intricate scientific abstracts into simpler, more accessible narratives? 
+Several prestigious journals have already initiated steps towards enhancing accessibility. 
+For instance, PNAS requires authors to submit Significance Statements understandable to an 'undergraduate-educated scientist', while Science includes an editor's abstract to provide a swift overview of the paper's salient points.
+
+In this project, our objective is to employ AI to rewrite scientific abstracts into easily understandable scientific narratives.
+To facilitate this, we have curated two new datasets: one containing PNAS abstract-significance pairs and the other encapsulating editor abstracts from Science.
+We utilize a Transformer model (a variant known as Flan-T5) to fine-tune our model for the task of simplifying scientific abstracts.
+Initially, the model is fine-tuned utilizing multiple discrete instructions by amalgamating four pertinent tasks in a challenge-proportional manner (a strategy we refer to as Multi-Instruction Pretuning).
+Subsequently, we continue the fine-tuning process exclusively with the abstract-significance corpus. Our model can generate lay summaries that outperform models fine-tuned solely with the abstract-significance corpus and models fine-tuned with traditional task combinations.
+We hope our work can foster a more comprehensive understanding of scientific research, enabling a larger audience to benefit from open science.
 
 
 - **Model type:** Language model