grill-lab/browsecomp-plus-passage-corpus-pyserini
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资源简介:
---
license: mit
task_categories:
- text-retrieval
tags:
- retrieval-augmented-generation
- deep-research
- search
---
# Passage Corpus (Pyserini Format) for the BrowseComp-Plus Dataset
This repository provides the passage corpus for the BrowseComp-Plus dataset in a format compatible with Pyserini, used in the paper [Revisiting Text Ranking in Deep Research](https://arxiv.org/abs/2602.21456), which has been accepted at **SIGIR 2026**, the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval.
- **Code:** [GitHub Repository](https://github.com/ChuanMeng/text-ranking-in-deep-research)
The corpus consists of 2,772,255 passages.
Unlike the version available at
https://huggingface.co/datasets/grill-lab/browsecomp-plus-passage-corpus, this release follows the Pyserini JSONL data format.
Each item contains two fields:
- `id`: the unique passage identifier.
- `contents`: the concatenation of the source document title and the passage text.
This format is directly compatible with Pyserini BM25.
## Sample Usage
As described in the [official repository](https://github.com/ChuanMeng/text-ranking-in-deep-research), you can build a Lucene index using Pyserini with the following command:
```bash
python -m pyserini.index.lucene \
--collection JsonCollection \
--input /path/to/downloaded/data/ \
--index ./indexes/index.bm25.passage \
--generator DefaultLuceneDocumentGenerator \
--threads 16 \
--storePositions --storeDocvectors --storeRaw
```
## Contact
If you have any questions or suggestions, please contact:
- [Chuan Meng](https://chuanmeng.github.io/): chuan.meng@ed.ac.uk
- [Litu Ou](https://leonard907.github.io/): litu.ou@ed.ac.uk
## Citation
If you find this work useful, please cite:
```bibtex
@inproceedings{meng2026revisiting,
title={Revisiting Text Ranking in Deep Research},
author={Meng, Chuan and Ou, Litu and MacAvaney, Sean and Dalton, Jeff},
booktitle={Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval},
year={2026}
}
```
提供机构:
grill-lab


