Approach to semantic similarity between documents
If I understand correctly, you're trying to map abstracts to their research papers.

Here is a simple starting point:

Compute a TF IDF model using the entire corpus (all abstracts + research papers). Use this model to transform your abstracts and research papers into a weighted vector representation. Under the TF IDF weighting scheme, these documents will be represented by vectors that point in the direction of the words that are most discriminating for them. Put another way, these vectors point towards the words that are the best at telling you what the document is about. So if two vectors are close to each other, then the two documents are likely to be about the same topic because they are using similar words.

This is where cosine similarity comes in. Take an abstract, and then iterate across all the research paper vectors, computing the cosine similarity between them. Then, map that abstract to whichever research paper has the highest cosine similarity. Repeat this for each abstract.

Once you have this simple baseline approach, you can start looking at more sophisticated models for capturing semantic similarity.