Yakk99/scihigh2026-subtask1-bart-titled

SciHigh-2026 Subtask 1 — bart-large-cnn titled (run 1)

Full fine-tune of bart-large-cnn.

Fine-tuned for the FIRE 2026 SciHigh shared task, Subtask 1: generating research highlights from scientific paper abstracts (MixSub corpus).

• Input format: <paper title> | <abstract> (corpus punctuation-stripped style; abstracts repaired via DOI-verified Semantic Scholar recovery)
• Output: 3-5 highlight sentences, ~55 words, greedy decoding
• Training: one epoch on 15,960 pairs, Kaggle dual T4
• Validation ROUGE-L F1 (plain LCS, no stemming): 26.37

Repository contents

file	purpose
train_final.py	standalone training recipe that produced this model
train_final.ipynb	notebook version of the same recipe
final/	full data pipeline: abstract recovery, title fetch, dataset build, submission validation

Data pipeline summary

40% of corpus abstracts are truncated mid-sentence. Each row was matched to its ScienceDirect PII (100% verified join), resolved to a DOI via Elsevier's keyless API, and its complete abstract recovered from Semantic Scholar under a label-free >=90%-token-overlap acceptance filter (95% of truncated rows repaired). Paper titles were fetched for 100% of rows and prepended to inputs. See final/README.md for the full pipeline and negative-results summary. Trained 2026-08-08.