SciHigh 2026 Task 2 — BART-large Title Generation Model
This repository contains a fine-tuned BART-large model for Task 2: Title Generation from Abstracts of the SciHigh 2026 shared task at FIRE 2026.
The model takes a scientific paper abstract as input and generates a concise research-paper title.
Base Model
- Model:
facebook/bart-large
- Architecture: BART encoder-decoder Transformer
- Parameters: approximately 406 million
- Framework: Hugging Face Transformers
Task
Input: Scientific paper abstract
Output: Generated scientific paper title
The model was fine-tuned for highly compressed abstractive generation, where the goal is to capture the central topic and contribution of an abstract in title form.
Dataset
The model was fine-tuned on the SpringerSSAT dataset released for SciHigh 2026 Task 2.
Dataset split used:
- Training: 2,778 abstract-title pairs
- Validation: 347 abstract-title pairs
- Test: 348 abstracts with masked reference titles
Only the training split was used for gradient updates. The validation set was used for evaluation and best-checkpoint selection.
Preprocessing
- Maximum abstract length: 512 tokens
- Maximum target-title length: 64 tokens
- Input truncation: enabled
- Target truncation: enabled
- Dynamic batch padding using
DataCollatorForSeq2Seq
- No task-specific instruction prefix was added
Fine-Tuning Configuration
- Epochs: 3
- Learning rate:
3e-5
- Weight decay:
0.01
- Per-device training batch size: 1
- Per-device evaluation batch size: 1
- Gradient accumulation steps: 8
- Effective training batch size: 8
- Mixed-precision training: FP16 AMP
- Gradient checkpointing: enabled
- Evaluation strategy: once per epoch
- Checkpoint saving strategy: once per epoch
- Generation beam size during validation: 4
- Maximum generation length: 64
- Best-model selection metric: ROUGE-L
- Best model automatically restored after training
Validation Results
| Epoch | Validation Loss | ROUGE-1 | ROUGE-2 | ROUGE-L | ROUGE-Lsum |
|---|
| 1 | 2.282688 | 44.9926 | 21.3081 | 36.6377 | 36.6539 |
| 2 | 2.217401 | 45.7446 | 22.2135 | 37.6433 | 37.6641 |
| 3 | 2.242662 | 45.2856 | 21.8391 | 37.2244 | 37.2773 |
Best Checkpoint
The best model was obtained at Epoch 2.
- ROUGE-1: 45.7446
- ROUGE-2: 22.2135
- ROUGE-L: 37.6433
- ROUGE-Lsum: 37.6641
- Validation loss: 2.217401
A separate evaluation over all 347 validation examples reproduced these results.
These are validation-set results and are not official hidden-test scores.
Example Usage
1from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
2
3model_name = "kckrish21/SciHigh2026-Task2-BART-large"
4
5tokenizer = AutoTokenizer.from_pretrained(model_name)
6model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
7
8abstract = """
9Insert a scientific paper abstract here.
10"""
11
12inputs = tokenizer(
13 abstract,
14 return_tensors="pt",
15 max_length=512,
16 truncation=True
17)
18
19generated_ids = model.generate(
20 **inputs,
21 max_length=64,
22 num_beams=4,
23 early_stopping=True
24)
25
26title = tokenizer.decode(
27 generated_ids[0],
28 skip_special_tokens=True
29)
30
31print(title)
Test-Set Generation
For SciHigh 2026 test inference:
- Test examples: 348
- Generation batch size: 4
- Beam size: 4
- Maximum generation length: 64
- Early stopping: enabled
- Missing predictions: 0
- Empty predictions: 0
The reference test titles are masked by the task organizers, so no test-set metric is reported here.
Intended Use
This model is intended for:
- scientific paper title generation from abstracts
- research on scientific text generation
- participation and reproducibility for SciHigh 2026 Task 2
Generated titles should be treated as model suggestions rather than authoritative paper titles.
Limitations
The model was fine-tuned on a relatively small dataset of social-science research abstracts. Performance may differ for scientific domains or writing styles that are substantially different from the SpringerSSAT training distribution.
Like other neural text-generation systems, the model may generate wording that is incomplete, overly generic, or not fully supported by the abstract.
Shared Task
SciHigh 2026 — Research Highlight Generation from Scientific Papers
FIRE 2026
Subtask 2: Title Generation from Abstracts
Ranking for the shared task is primarily based on ROUGE-L F1.
