---
title: "PodBench — paper by Xu et al. (2026) — summary and ideas"
description: "Many AI podcast products, such as NotebookLM, first have an LLM write a multi-speaker dialogue from user requests and… 39 references."
authors: ["Chenning Xu", "Mao Zheng", "Mingyu Zheng", "Mingyang Song"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.2019/"
doi: "10.18653/v1/2026.acl-long.2019"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/xu-2026-podbench-comprehensive-benchmark-instruction-aware/"
---
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# PodBench: A Comprehensive Benchmark for Instruction-Aware Audio-Oriented Podcast Script Generation

**Authors:** Chenning Xu, [Mao Zheng](https://md.research.elamur.ai/free/en/authors/mao-zheng/), [Mingyu Zheng](https://md.research.elamur.ai/free/en/authors/mingyu-zheng/) and [Mingyang Song](https://md.research.elamur.ai/free/en/authors/mingyang-song/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.2019/) · [PDF](https://aclanthology.org/2026.acl-long.2019.pdf) · [DOI 10.18653/v1/2026.acl-long.2019](https://doi.org/10.18653/v1/2026.acl-long.2019) · [OpenAlex](https://openalex.org/W7166825614)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Many AI podcast products, such as NotebookLM, first have an LLM write a multi-speaker dialogue from user requests and source documents, and then voice it with TTS. Despite this, there has been no dedicated benchmark for the script-writing step. The authors (Tencent) built PodBench to fill that gap. It contains 800 test samples that pair Chinese and English source materials with instructions synthesized by an LLM. The materials cover single- and multi-document settings across 12 domains, and the instructions combine eight types of requirements, including language, speaker count, focus and structure. Prompts can reach roughly 21K tokens.

The evaluation scores instruction following separately from podcast-specific script quality. It combines deterministic checks with LLM-based judging under domain-specific, weighted rubrics.

The authors tested open-source, proprietary and writing-specialized LLMs:
* Proprietary models score best overall.
* Open models that use explicit reasoning cope better with long inputs and with coordinating several speakers.
* Across models, following instructions closely does not guarantee substantive content.

Because PodBench measures only the script stage, it complements PodEval, which assesses audio output. The benchmark is publicly released.

## Key points
- **Data sources:** Documents come from LongWanjuan, OpenNewsArchive and WanJuan (Chinese), and from WanJuan-CC and Pile Arxiv (English). Every source is distributed under an open license, for example CC BY 4.0 or MIT.
- **Multi-document inputs:** Gemini-2.5-pro produced a topic query for each document. The documents were then grouped by clustering Qwen3-Embedding-4B embeddings of those queries.
- **Instruction requirements:** The eight requirement dimensions include Focused Content, Core Intent, Script Structure, Specific Expression and Podcast Language, among others.
- **Agreement with human judgments:** On 200 held-out queries, the PodBench protocol matched the human majority preference 86.80% of the time. This compares with 79.60% for a generic checklist, 83.22% for an unweighted domain rubric, and 83.36% average agreement between human annotators.
- **Availability:** Code and data are at https://github.com/xucncn/PodBench. Prosody and speech quality are explicitly out of scope.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Tencent](https://md.research.elamur.ai/free/en/companies/tencent/) — Company, Shenzhen, China · 163 works · 21,032 citations

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## Cite

Cite as: Chenning Xu, Mao Zheng, Mingyu Zheng and Mingyang Song. 2026. PodBench: A Comprehensive Benchmark for Instruction-Aware Audio-Oriented Podcast Script Generation. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{xu2026podbench,
  title = {{PodBench: A Comprehensive Benchmark for Instruction-Aware Audio-Oriented Podcast Script Generation}},
  author = {Chenning Xu, Mao Zheng, Mingyu Zheng and Mingyang Song},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.2019},
  url = {https://doi.org/10.18653/v1/2026.acl-long.2019}
}
```

- [← previous POaaS: Minimal-Edit Prompt Optimization as a Service to Lift Accuracy and Cut Hallucinations on On-Device sLLMs](https://md.research.elamur.ai/free/en/papers/shim-2026-poaas-minimal-edit-prompt-optimization/)
- [next → PoETIC: A Re-framing of Context Dependent Emotion Detection](https://md.research.elamur.ai/free/en/papers/surange-2026-poetic-re-framing-context-dependent/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/45/)

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37. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, Chujie Zheng, Dayiheng Liu, Fan Zhou, Fei Huang, Feng Hu, Hao Ge, Haoran Wei, Huan Lin, Jialong Tang, and 41 others. 2025. [Qwen3 technical report](https://md.research.elamur.ai/free/en/papers/yang-2025-qwen3-technical-report/). Preprint, arXiv:2505.09388.
38. Team Seedream, Yunpeng Chen, Yu Gao, Lixue Gong, Meng Guo, Qiushan Guo, Zhiyao Guo, Xiaoxia Hou, Weilin Huang, Yixuan Huang, Xiaowen Jian, Huafeng Kuang, Zhichao Lai, Fanshi Li, Liang Li, and et al. 2025. Seedream 4.0: Toward next-generation multimodal image generation. Preprint, arXiv:2509.20427. — [arXiv:2509.20427](https://arxiv.org/abs/2509.20427) [find](https://scholar.google.com/scholar?q=Team+Seedream%2C+Yunpeng+Chen%2C+Yu+Gao%2C+Lixue+Gong%2C+Meng+Guo%2C+Qiushan+Guo%2C+Zhiyao+Guo%2C+Xiaoxia+Hou%2C+Weilin+Huang%2C+Yixuan+Huang%2C+Xiaowen+Jian%2C+Huafeng+Kuang%2C+Zhichao+Lai%2C+Fanshi+Li%2C+Liang+Li%2C+and+et+al%2E+2)
39. Shangqing Tu, Yucheng Wang, Daniel Zhang-Li, Yushi Bai, Jifan Yu, Yuhao Wu, Lei Hou, Huiqin Liu, Zhiyuan Liu, Bin Xu, and Juanzi Li. 2025. Longwriter-v: Enabling ultra-long and high-fidelity generation in vision-language models. Preprint, arXiv:2502.14834. — [arXiv:2502.14834](https://arxiv.org/abs/2502.14834) [find](https://scholar.google.com/scholar?q=Shangqing+Tu%2C+Yucheng+Wang%2C+Daniel+Zhang%2DLi%2C+Yushi+Bai%2C+Jifan+Yu%2C+Yuhao+Wu%2C+Lei+Hou%2C+Huiqin+Liu%2C+Zhiyuan+Liu%2C+Bin+Xu%2C+and+Juanzi+Li%2E+2025%2E+Longwriter%2Dv%3A+Enabling+ultra%2Dlong+and+high%2Dfidelity+generatio)

*Updated 2 November 2026*