Assignment for Joyita Di Sayandip Bhattacharyya #3rd Year, B. Tech, CSE 2021-2025 Batch, B. P. Poddar Institute Of Management and Technology

My Approach for this task:

1. Data Reading and Preprocessing:

• I began by reading the citation network dataset (cit-HepTh.txt) and the abstracts dataset (cit-HepTh-abstracts.tar.gz).
• The citation network dataset provides information about which papers cite other papers.
• In the abstracts dataset, I found abstracts of papers organized by year.

2. Loading Pretrained Sci-BERT Model:

• I loaded the pretrained Sci-BERT model (allenai/scibert_scivocab_uncased) using the Sentence Transformers library.
• This model, trained specifically for scientific text, allows me to generate embeddings for sentences or paragraphs.

3. Iterating Over Seed Papers:

• I selected a few seed papers (e.g., '9201001', '9203201', '119203001') for which I wanted to calculate similarity scores.
• For each seed paper:
  • If the paper exists in the citation network dataset, I proceeded to find its references and their abstracts.

4. Embedding Abstracts and Calculating Similarity:

• For each reference paper cited by the seed paper:
  • I embedded the abstracts of both the seed paper and the reference paper using the Sci-BERT model.
  • Then, I calculated the cosine similarity between the embeddings of the seed paper's abstract and the reference paper's abstract.
  • Cosine similarity serves as a similarity metric here, indicating how similar the abstracts are.
  • Higher similarity scores suggest greater thematic similarity between papers.

5. Output:

• I printed the reference paper IDs along with their similarity scores for each seed paper.
• This information helps in understanding the thematic similarity between papers based on their abstract content.