meetingbank-eval
MeetingBank: A Benchmark Dataset for Meeting Summarization — Hu et al. (2023) (arXiv:2305.17529, 2023)
What this evaluates
Evaluates the quality of abstractive and extractive summarization systems on city council meeting transcripts and videos. It probes a model's ability to capture key decisions, maintain factual accuracy, and produce fluent, coherent, and non-redundant summaries.
Datasets
- MeetingBank — total 1366; splits: train (-1), test (200)
Metrics
Average Score (primary) — range: [1, 5]
- Mean of five 5-point Likert scale scores: informativeness, factuality, fluency, coherence, and redundancy. Higher values indicate better summary quality.
Input / output format
Input: Video segment (typically ≤30 minutes), corresponding transcript, and a system-generated summary.
Output: Five scores (1–5) for each criterion, plus an averaged total score.
Scoring recipe
def compute_average_score(predictions):
criteria = ['informativeness', 'factuality', 'fluency', 'coherence', 'redundancy']
scores = [predictions[c] for c in criteria]
return sum(scores) / len(scores)
Common pitfalls
- Summaries must be presented in random order to evaluators to prevent order-based bias.
- Human evaluation is only performed on a subset of 200 randomly selected instances, not the full dataset.
- Evaluators assess both the video and transcript alongside the summary, which differs from text-only benchmarks.
Evidence (verbatim from paper)
The workers are asked to watch a video segment, typically 30 minutes or less, read the transcript, and then evaluate the quality of each system summary based on five criteria: informativeness, factuality, fluency, coherence, and redundancy. A 5-point Likert scale is used to evaluate each criterion. The scores are then averaged, and standard deviation is also reported. ... AVERAGE SCORE
Citation
@misc{hu2023meetingbank,
title={MeetingBank: A Benchmark Dataset for Meeting Summarization},
author={Hu et al. (2023)},
year={2023},
note={arXiv:2305.17529}
}
1---2name: meetingbank-eval3description: Evaluates the quality of abstractive and extractive summarization systems on city council meeting transcripts and videos. It probes a model's ability to capture key decisions, maintain factual accuracy, and produce fluent, coherent, and non-redundant summaries. Use when the user wants to benchmark on MeetingBank, or asks about evaluating this task. Reports Average Score.4---56# meetingbank-eval78> MeetingBank: A Benchmark Dataset for Meeting Summarization — Hu et al. (2023) (arXiv:2305.17529, 2023)910## What this evaluates1112Evaluates the quality of abstractive and extractive summarization systems on city council meeting transcripts and videos. It probes a model's ability to capture key decisions, maintain factual accuracy, and produce fluent, coherent, and non-redundant summaries.1314## Datasets1516- **MeetingBank** — total 1366; splits: train (-1), test (200)1718## Metrics1920- `Average Score` **(primary)** — range: [1, 5]21 - Mean of five 5-point Likert scale scores: informativeness, factuality, fluency, coherence, and redundancy. Higher values indicate better summary quality.2223## Input / output format2425**Input**: Video segment (typically ≤30 minutes), corresponding transcript, and a system-generated summary.2627**Output**: Five scores (1–5) for each criterion, plus an averaged total score.2829## Scoring recipe3031```python32def compute_average_score(predictions):33 criteria = ['informativeness', 'factuality', 'fluency', 'coherence', 'redundancy']34 scores = [predictions[c] for c in criteria]35 return sum(scores) / len(scores)36```3738## Common pitfalls3940- Summaries must be presented in random order to evaluators to prevent order-based bias.41- Human evaluation is only performed on a subset of 200 randomly selected instances, not the full dataset.42- Evaluators assess both the video and transcript alongside the summary, which differs from text-only benchmarks.4344## Evidence (verbatim from paper)4546> The workers are asked to watch a video segment, typically 30 minutes or less, read the transcript, and then evaluate the quality of each system summary based on five criteria: informativeness, factuality, fluency, coherence, and redundancy. A 5-point Likert scale is used to evaluate each criterion. The scores are then averaged, and standard deviation is also reported. ... AVERAGE SCORE4748## Citation4950```bibtex51@misc{hu2023meetingbank,52 title={MeetingBank: A Benchmark Dataset for Meeting Summarization},53 author={Hu et al. (2023)},54 year={2023},55 note={arXiv:2305.17529}56}57```5859- arXiv: 2305.17529