Hello, I am currently working on a final year project. I have decided that I want to work with summarising textbooks using seq2seq.

I have found some implementations of seq2seq text summarization using TensorFlow on GitHub, so I have examples of working solutions I can learn from.

I have run into the problem of not being able to find any datasets specifically where textbooks are given with summaries. 

However, I have managed to find datasets for other types of texts; [TensorFlow's Scientific papers dataset](https://www.tensorflow.org/datasets/catalog/scientific_papers). Where papers are given with abstracts (which can be treated as a summary).

I am not an expert in ML, but I am considering using these datasets and then fine-tuning the model using a small dataset I will manually create with Textbooks and summaries. 

There are some similar characteristics between the data, so I would assume that the model would be able to transfer some things it learns from the scientific papers to the textbooks.

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I do not have the slightest clue about how reasonable or successful this might turn out. I am looking any advice, resources or alternatives to what I have mentioned above.

*What do you do when the dataset for the problem you're trying to solve does not exist?* 

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Thank you.