---
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.