README.md

metadata

license: other
license_name: postertext-30k-research-use
license_link: LICENSE
task_categories:
  - text-generation
language:
  - en
pretty_name: PosterText-30K
size_categories:
  - 10K<n<100K
tags:
  - academic-poster-generation
  - instruction-tuning
  - text-generation
  - document-understanding
  - poster-generation

PosterText-30K

PosterText-30K is a section-level dataset for budget-conditioned academic poster text generation. Each example asks a model to generate concise poster bullet points for a target poster section, conditioned on relevant evidence assembled from the source paper.

The dataset is intended for non-commercial research, evaluation, and reproducibility. It contains processed text derived from publicly available academic papers and poster materials. See the license note below before use.

Files

File	Description
train.jsonl	Training split
validation.jsonl	Validation split
source_manifest.jsonl	Source-level provenance manifest with public conference poster-page URLs
code/postereval/	PosterEval metric code for structural and semantic evaluation
LICENSE	Research-use license

Schema

Each JSONL row has three fields:

Field	Type	Description
instruction	string	Task instruction specifying the target poster role and bullet count
input	string	Relevant paper evidence plus the target poster section title
output	string	Poster bullet text extracted from the cleaned poster structure

Example:

{
  "instruction": "Generate 2 bullet points for the \"Experimental Results / Performance Analysis\" section of an academic poster.",
  "input": "## Paper Content\\n\\n# Paper Title\\n\\n...\\n\\n## Task\\n\\nSection Title: ...\\n\\nGenerate poster content for this section.",
  "output": "• Compares FID scores on MSCOCO dataset\\n• SiD2 achieves 8.1 FID with bias=-3 and 6.7 FID with 16-step distilled version"
}

Splits

Split	Examples	File size	SHA256
train	36,887	438,284,309 bytes	25fe02a861809b5e76759913e22e5a532e627680978abce10179b9f06aa10a61
validation	4,098	48,308,618 bytes	f1aac6501fc10820b852da997e613c4c40d9c37bda844ed96b20d4172fcd9e3f
total	40,985	486,592,927 bytes	-

The split is a section-level random split with validation ratio 0.1 and seed 42 during dataset construction.

Source Corpus

PosterText-30K is constructed from 7,060 paper-poster pairs:

Source	Paper-poster pairs
CVPR 2024	2,198
CVPR 2025	2,600
ECCV 2024	1,443
ICLR 2025	374
ICML 2025	199
NeurIPS 2024	246

The release includes source_manifest.jsonl, one row per source paper-poster pair. Each row contains source_id, conference, and public_source_page_url. The URL points to the conference virtual poster page, where the paper PDF and poster asset are available when provided by the conference site. The released train/validation JSONL files do not include per-example source IDs, because the construction script did not store them in the final instruction-tuning examples.

Construction Pipeline

1. Paper PDFs are parsed into Markdown with MinerU.
2. Poster images are parsed into structured JSON with a VLM parser.
3. Poster structures are schema-validated and automatically cleaned.
4. Relevant paper sections are selected according to each poster section's cognitive role.
5. Section-level instruction/input/output examples are generated from cleaned poster sections.

The full training-data construction scripts are maintained separately and are not included in this dataset upload. This release includes the PosterEval evaluation code used to compute the reported poster metrics.

Evaluation Code

The code/postereval/ directory contains a compact PosterEval evaluation toolkit for NeurIPS E&D artifact review:

File	Purpose
evaluate_structural_pptx.py	Computes Ove, Ali, and Ofl from PPTX geometry
prepare_pptx_autofit.py	Optionally materializes text-frame autofit geometry before PPTX evaluation
prepare_ir.py	Generates content and figure IR from rendered poster images
evaluate_semantic_ir.py	Computes Order, Completeness, LTA, and Claim F1 from IR files
openrouter_client.py	OpenRouter-compatible JSON client for VLM/LLM parsing and claim matching
qwen3_vl_embedding.py	Lightweight local wrapper for Qwen3-VL-Embedding-2B used by LTA
prompts/	Public prompts for content IR, figure IR, and claim-pair scoring

The code directory does not include model weights, generated posters, rendered poster images, raw PDFs, API keys, or local runtime artifacts. See code/postereval/README.md for installation and usage details.

Quality Control

The source corpus contains 7,060 raw poster structures and 7,060 cleaned poster structures. During validation, 41 raw parses produced validation-error sidecar files; the release is built from the cleaned structures.

Aggregate text length statistics:

Field	Average chars	Min chars	Max chars
input	11,256.1	141	265,324
output	290.1	15	1,608

Intended Use

This dataset is intended for:

• non-commercial research on academic poster generation;
• budget-conditioned or section-conditioned summarization;
• evaluation of poster text compression and content selection;
• reproducibility of the PosterText-30K experiments.

It is not intended for commercial model training, commercial products, or redistribution of third-party source materials outside research use.

Limitations and Rights

The validation split is a section-level random split rather than a strict paper-heldout or venue-heldout benchmark. It is intended for model development and reproducibility, not as a standalone generalization test across papers, venues, or research areas.

The construction pipeline relies on automatic PDF parsing, VLM-based poster parsing, schema validation, and cleaning. Residual OCR, parsing, section-mapping, or bullet-extraction errors may remain.

The dataset contains processed text derived from publicly available academic papers and poster materials. Copyright and other rights in the original papers, posters, figures, and associated source materials remain with their respective authors, publishers, or rights holders. Users are responsible for complying with the licenses, terms, and restrictions of the original sources.

Loading

from datasets import load_dataset

dataset = load_dataset(
    "json",
    data_files={
        "train": "train.jsonl",
        "validation": "validation.jsonl",
    },
)

License Note

This dataset is released under the PosterText-30K Research Use License (license: other). Some examples contain processed text derived from publicly available academic papers and poster materials. We do not claim ownership over third-party source materials. Users are responsible for complying with the licenses, terms, and restrictions of the original sources.

If you are a rights holder and believe content should be removed or modified, please contact the dataset maintainers through the dataset repository.

Citation

Citation information will be added after the associated paper is finalized.