metadata
license: apache-2.0
base_model: facebook/bart-large-cnn
tags:
- summarization
- scientific-papers
- highlights
- scihigh-2026
- fire-2026
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.