Blame for README.md

---
license: cc-by-4.0
language:
  - en
pretty_name: "Mind The Gap (MTG-AD): Dataset and Fine-grained Evaluation for End-to-End Inline Audio Descriptions"
size_categories:
  - n<1K
task_categories:
  - video-text-to-text
  - text-generation
tags:
  - audio-description
  - video-accessibility
  - blind-and-low-vision
  - multimodal-evaluation
  - user-generated-video
  - inline-audio-description
  - NeurIPS-2026
  - mtg-ad
  - mtg-ad-eval
configs:
  - config_name: annotations
    default: true
    data_files:
      - split: validation
        path: data/annotations/validation.jsonl
      - split: test
        path: data/annotations/test.jsonl
  - config_name: model_outputs
    data_files:
      - split: test
        path: data/model_outputs/test.jsonl
---

# Mind The Gap (MTG-AD): Dataset and Fine-grained Evaluation for End-to-End Inline Audio Descriptions

**Mind The Gap (MTG-AD)** is a professionally annotated dataset of 433 YouTube user-generated videos (~36 hours) paired with
*inline* audio descriptions (AD) co-created by paired blind and sighted AD professionals. Inline AD must fit within the
natural silences of a video, so the dataset benchmarks the full **end-to-end inline AD task**: generating coherent timed
narration scripts that describe the important visual content without overlapping the original speech.

This repository accompanies the NeurIPS 2026 (Evaluations & Datasets Track) paper
*Mind The Gap: Dataset and Fine-grained Evaluation for End-to-End Inline Audio Descriptions*.
The evaluation code (**MTG-AD-Eval**), AD generation prompts, and TTS-timing toolkit are at
[github.com/google/mtg-ad](https://github.com/google/mtg-ad).

[Paper](https://www.alphaxiv.org/abs/2610.39955) · [Dataset](https://huggingface.co/datasets/google/mind-the-gap-inline-ad) · [Code](https://github.com/google/mtg-ad)

## Dataset Summary

| Property | Value |
|---|---|
| **Videos** | 433 YouTube user-generated videos (332 validation / 101 test) |
| **Total duration** | ~35.9 hours |
| **Average video length** | ~5 minutes |
| **Ground-truth AD segments** | 8,126 total (6,214 validation / 1,912 test; median 16 per video) |
| **Categories** | Vlog (201), Education (71), Cooking (49), Nature (48), Travel (36), Science (23), Fashion (4), Other (1) |
| **Language** | English |
| **License** | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) |

## Configurations and Splits

The repository has two configurations (subsets). They have different schemas and granularity, so they are kept as
separate configs of the same dataset.

| Config | Split | Rows | Granularity | Contents |
|---|---|---|---|---|
| `annotations` (default) | `validation` | 332 | one row per video | ground-truth AD, dense visual info, ASR transcript, metadata |
| `annotations` (default) | `test` | 101 | one row per video | ground-truth AD, dense visual info, ASR transcript, metadata |
| `model_outputs` | `test` | 1,193 | one row per (video, model configuration) | ADs generated by 12 model/prompt configurations + per-video metric scores |

```python
from datasets import load_dataset

ann = load_dataset("google/mind-the-gap-inline-ad", "annotations")       # validation + test
val, test = ann["validation"], ann["test"]

preds = load_dataset("google/mind-the-gap-inline-ad", "model_outputs", split="test")
gemini = preds.filter(lambda r: r["config_name"] == "gemini_2p5_flash_timing_asr")
```

The **test** split is the 101-video evaluation subset used for all multi-model results in the paper. The **validation**
split contains the remaining 332 videos and can be used for prompt development, few-shot example selection, or
fine-tuning/validation. There is no training split.

### Coverage of auxiliary files

| | validation (332) | test (101) |
|---|---|---|
| Ground-truth AD | 332 | 101 |
| Dense visual info | 330 (305 Gemini 2.5 Flash, 25 Gemini 3.7 Flash) | 101 (Gemini 2.5 Flash) |
| ASR transcript | 332 (94 empty) | 101 (20 empty) |
| Model outputs + metrics | – | 101 per configuration (82 for `qwen3p5_omni_flash`), 12 configurations |

An empty transcript (`""`, empty file) means the video has no transcribed speech. Where a visual-info file is
unavailable, the `visual_info` field is `null` and there is no file under `raw/`.

## Data Fields

### `annotations`

| Field | Type | Description |
|---|---|---|
| `video_id` | string | 11-character YouTube video ID |
| `youtube_url` | string | `https://www.youtube.com/watch?v={video_id}` |
| `split` | string | `validation` or `test` |
| `category` | string | Content category of the video |
| `duration` | string | Video duration as `M:SS` |
| `duration_seconds` | int | Video duration in seconds |
| `num_ad_segments` | int | Number of ground-truth AD segments |
| `gt_ad` | string | Raw ground-truth AD script (one segment per line, see format below) |
| `gt_ad_segments` | list of {`start_time`, `start_seconds`, `ad_type`, `text`} | Parsed ground-truth AD segments |
| `visual_info` | string or null | Dense, timestamped visual information generated by a Gemini model (visual events, on-screen text, people, summary) |
| `visual_info_model` | string or null | Model that generated `visual_info`: `gemini-2.5-flash` or `gemini-3.7-flash` |
| `transcript` | string | Raw timestamped YouTube ASR transcript (empty string if the video has no transcribed speech) |
| `transcript_segments` | list of {`start_time`, `end_time`, `start_seconds`, `end_seconds`, `text`} | Parsed transcript segments (empty list if no speech) |

### `model_outputs`

| Field | Type | Description |
|---|---|---|
| `video_id` | string | YouTube video ID (test split) |
| `config_name` | string | Model/prompt configuration identifier (see table below) |
| `model` | string | Model name |
| `prompt` | string | Prompting strategy |
| `model_ad` | string | Model response as produced (not edited), including any preamble/reasoning the model emitted before the timestamped AD script |
| `model_ad_source` | string | Where the response comes from (`original` for the first 9 configurations; otherwise the source directory) |
| `metrics_source` | string or null | Where the metric file comes from; `null` if no metric file exists for that row |
| `include_in_scoring` | bool | `false` for rows that are not part of the paper's evaluation and should be left out of aggregate metrics (the 25 unstandardized `qwen3p5_omni_flash` responses) |
| `exclusion_reason` | string or null | Why a row is excluded from scoring; `null` if included |
| *metric columns* | float or null | Per-video numeric metric values (see [Metrics](#metrics)); `null` when a metric could not be computed for that video |
| `metrics_json` | string or null | Verbatim content of the per-video metric JSON file (newer configurations include extra fields such as the AD text, transcript, and judge reasoning) |

| `config_name` | Model | Prompt |
|---|---|---|
| `gemini_2p5_flash_timing_asr` | Gemini 2.5 Flash | Guideline + Timing ASR |
| `gemini_2p5_flash_guideline_asr` | Gemini 2.5 Flash | Guideline + ASR |
| `gemini_2p5_flash_guideline` | Gemini 2.5 Flash | Guideline only |
| `gemini_2p5_flash_simple` | Gemini 2.5 Flash | Simple AD |
| `claude_4_5` | Claude 4.5 | Guideline + Timing ASR |
| `claude_4` | Claude 4 | Guideline + Timing ASR |
| `gpt_4_1_mini` | GPT-4.1 Mini | Guideline + Timing ASR |
| `gpt_5_2` | GPT 5.2 | Guideline + Timing ASR |
| `qwen_vl_max` | Qwen-2-VL-Max | Guideline + Timing ASR |
| `gemini_3p5_flash_timing_asr` | Gemini 3.5 Flash | Guideline + Timing ASR |
| `qwen3p5_omni_flash` | Qwen-3.5-Omni-Flash | Guideline + Timing ASR |
| `qwen3p5_omni_plus` | Qwen-3.5-Omni-Plus | Guideline + Timing ASR |

## Raw File Formats

The same content is also provided as plain-text files under `raw/`, in the layout expected by the [evaluation toolkit](https://github.com/google/mtg-ad):

```
raw/
├── validation/
│   ├── gt_ad/output_{VIDEO_ID}.txt
│   ├── visual_info/output_{VIDEO_ID}.txt
│   └── transcript/output_{VIDEO_ID}.txt
└── test/
    ├── gt_ad/output_{VIDEO_ID}.txt
    ├── visual_info/output_{VIDEO_ID}.txt
    ├── transcript/output_{VIDEO_ID}.txt
    ├── model_ads/{CONFIG_NAME}/output_{VIDEO_ID}.txt
    ├── model_metrics/{CONFIG_NAME}/output_{VIDEO_ID}.json
    └── scoring_exclusions.json   # {config: [video_id, ...]} rows to leave out of aggregate metrics
```

Video ID lists: `video_ids.txt` (all 433), `validation_video_ids.txt` (332), `test_video_ids.txt` (101).

**Ground-truth AD** (`gt_ad`): one segment per line; the timestamp is the segment start time, followed by the AD type
and a Unicode line separator (U+2028) before the description text:
```
HH:MM:SS.mmm-STANDARD<U+2028>description text
```

**Visual info** (`visual_info`): free-form model output containing timestamped visual events
(`HH:MM:SS.mmm- event description`), on-screen text, descriptions of people, and a video summary.

**Transcript** (`transcript`): one ASR segment per line:
```
HH:MM:SS.mmm--HH:MM:SS.mmm spoken text
```

**Model metrics** (`model_metrics/*.json`): a flat JSON object of metric values. In the JSONL `model_outputs` config,
`.` in key names is replaced with `_` (e.g. `metric_G1.1_precision_avg_ad_in_visual_info` →
`metric_G1_1_precision_avg_ad_in_visual_info`).

## Metrics

The evaluation suite (**MTG-AD-Eval**) measures generated AD along dimensions derived from professional AD guidelines. Main per-video
metric columns and the paper metrics they correspond to:

| Paper metric | Column | Direction |
|---|---|---|
| Prioritization (G1.1) | `metric_G1_1_precision_avg_ad_in_visual_info` | ↑ |
| No Redundancy (G1.2) | `metric_2_1_does_not_restate` | ↑ |
| On-Screen Text (G1.3) | `metric_1_2_narrates_onscreen_text` (meaningful when `onscreen_text_present` = 1) | ↑ |
| Timeliness (G2) | `metric_g_2_avg_diff_time` (seconds) | ↓ |
| Audio Overlaps (G7) | `total_overlap` / `tts_ad_transcript_overlap_seconds` (seconds, TTS at `tts_rate` = 1.2×) | ↓ |
| Avg. Silence Duration (G8) | `avg_silence_duration_w_ad_tts` (seconds) | ↓ |
| Audio Coverage Ratio (G8) | `ad_total_speaking_time_per_silent_second` | ≤ 1 (higher is better up to 1; values > 1 mean AD time exceeds available silence) |
| VideoA11y-Ref | `videoa11y_descriptive`, `videoa11y_objective`, `videoa11y_accurate`, `videoa11y_clear` (1–5 Likert) | ↑ |

Additional columns describe AD/GT word counts and durations (`ad_total_words`, `gt_total_time`, …), speech/silence
statistics of the video (`total_silence_time`, `dialog_density`, …), and diagnostics (`metric_g_2_matched_visual_info_ratio`,
`metric_g_2_diffs_over_ten`, `metric_G1_1_precision_avg_ad_in_visual_info_std`).

> [!NOTE]
> Aggregate numbers in the paper are computed from these per-video values with the aggregation in the [code release](https://github.com/google/mtg-ad)
> (`analysis/aggregate.py`, `analysis/paper_tables_and_figures.ipynb`): rows with `include_in_scoring = false` are
> dropped, On-Screen Text only counts videos with on-screen text, timing values longer than the video are ignored, the
> prompt comparison uses only the videos that have outputs from every prompt, and the model comparisons score each model
> on all test videos it has output for. Naive column means therefore differ from the reported numbers.

## Data Collection and Curation

### Video selection
Videos were sourced from YouTube channels with at least 1,000 subscribers, selecting videos around 3–7 minutes long. An
initial candidate pool of 557 videos was reviewed by the professional AD team. Videos were excluded if they contained
non-stop narration leaving no temporal gaps, or skipped for redundancy after annotating 3–5 videos of the same theme.

### Annotation process
Audio descriptions were written by **pairs of professional AD writers: one blind and one sighted**.
1. The sighted writer watched each video in full, then re-watched it in segments to draft concise, time-aligned
   descriptions of visual information not conveyed by the existing audio, including on-screen text.
2. The draft was imported into an editing environment to test and refine timing against the original audio track so
   that descriptions fit within natural pauses.
3. The blind writing partner performed a comprehensive quality-control review of clarity and effectiveness.

### Visual information and transcripts
Dense visual information is synthetic (model-generated). It was generated with Gemini 2.5 Flash for 406 videos
(including all test videos) and with Gemini 3.7 Flash, using the visual-info prompt in the [evaluation code](https://github.com/google/mtg-ad), for 25
validation videos that lacked it (see `visual_info_model`). Two validation videos (`6efEHcwNMZw`, `Ay3oHsgFgdE`) have no
visual info because the model could not access them by YouTube URL. ASR
transcripts are YouTube automatic speech recognition transcripts.

### Model-generated ADs (test split)
For the 101 test videos, ADs were generated by frontier multimodal models with several prompting strategies (see the
`config_name` table) and scored with the evaluation suite. Model responses and metric files are released as produced,
without edits. Notes on the newer configurations (`gemini_3p5_flash_timing_asr`, `qwen3p5_omni_flash`,
`qwen3p5_omni_plus`):

- **`qwen3p5_omni_flash`** covers 82 of the 101 test videos (no output is available for the other 19). For 57
  videos the response is a version with timestamps standardized to `H:MM:SS.mmm-STANDARD text`; these are the responses
  evaluated in the paper. The remaining 25 are the model's original responses as-is: they have format or validity
  issues (e.g. time ranges without descriptions, malformed JSON, or API error messages instead of an AD), and their
  metrics were computed on those original responses. They are kept for reference but marked
  `include_in_scoring = false` (and listed in `raw/test/scoring_exclusions.json`); leave them out when aggregating.
- **`qwen3p5_omni_plus`** has no metric file for 1 video (`CJv4d-XUFDQ`) and an empty metric file for 1 video
  (`68p4YCz7Ga4`); metric columns are `null` for both.
- Metric files for these configurations come from a separate evaluation run and include extra fields (kept as-is).
  With the exclusions above, they reproduce the paper's model comparison tables and figures exactly (see below).

## Key Results (from the paper)

Mean ± 95% CI half-width (1.96 · SEM) over videos, as in the paper's appendix tables. ↑ higher is better, ↓ lower
is better; Audio Coverage is better when higher only up to 1 (values above 1 mean the AD is longer than all available
silence). **Bold** marks the best value in each column.

### Prompt strategy comparison (Gemini-2.5 Flash; paper Tab. 4)

The 372 validation + test videos for which every prompt has output. The Human AD row is the professional ground-truth
AD, rendered with the same TTS (1.2×) and scored with the same metrics.

| Prompt | Prioritization ↑ | No Redundancy ↑ | On-Screen Text ↑ | Timeliness (s) ↓ | Overlaps w. TTS (s) ↓ | Audio Coverage (≤1) | Avg. Silence (s) ↓ |
|---|---|---|---|---|---|---|---|
| Simple AD | 0.75 ± 0.02 | **0.77 ± 0.03** | 0.47 ± 0.03 | 6.46 ± 3.03 | 15.07 ± 3.01 | 0.41 ± 0.06 | 17.95 ± 3.46 |
| Guideline only | **0.79 ± 0.02** | 0.72 ± 0.02 | **0.69 ± 0.03** | 3.27 ± 1.29 | 59.19 ± 7.28 | 1.23 ± 0.11 | 8.85 ± 3.13 |
| Guideline + ASR | **0.79 ± 0.02** | 0.71 ± 0.02 | 0.65 ± 0.03 | 5.11 ± 2.07 | 21.34 ± 4.64 | **0.83 ± 0.08** | 6.71 ± 2.47 |
| Gd. + Timing ASR | 0.76 ± 0.02 | 0.69 ± 0.03 | 0.63 ± 0.03 | 4.43 ± 1.17 | 12.77 ± 3.88 | 0.78 ± 0.05 | 8.97 ± 2.80 |
| *Human AD* | 0.73 ± 0.02 | 0.72 ± 0.02 | 0.61 ± 0.03 | **2.90 ± 0.48** | **7.51 ± 1.42** | 0.61 ± 0.03 | **4.19 ± 1.13** |

<details>
<summary>VideoA11y-Ref scores (1–5) for the prompt comparison</summary>

| Prompt | Descriptive ↑ | Objective ↑ | Accurate ↑ | Clear ↑ |
|---|---|---|---|---|
| Simple AD | 3.46 ± 0.13 | 3.55 ± 0.13 | 1.82 ± 0.11 | 2.59 ± 0.13 |
| Guideline only | 3.28 ± 0.14 | 3.59 ± 0.13 | 2.18 ± 0.12 | 2.85 ± 0.14 |
| Guideline + ASR | 2.81 ± 0.11 | 3.27 ± 0.12 | 1.74 ± 0.10 | 2.57 ± 0.12 |
| Gd. + Timing ASR | 3.03 ± 0.10 | 2.98 ± 0.13 | 1.64 ± 0.09 | 2.68 ± 0.11 |
| *Human AD* | **4.72 ± 0.07** | **4.93 ± 0.03** | **4.98 ± 0.02** | **4.96 ± 0.03** |

VideoA11y-Ref compares each AD with the human AD, so the Human AD row only serves as an upper bound.

</details>

The released `model_outputs` cover the test split only, so recomputing this comparison from them uses the 101 test
videos and gives slightly different values.

### Initial frontier models (Gd. + Timing ASR prompt; paper Tab. 5)

Each model is scored on all 101 test videos.

| Model | Prioritization ↑ | No Redundancy ↑ | On-Screen Text ↑ | Timeliness (s) ↓ | Overlaps w. TTS (s) ↓ | Audio Coverage (≤1) | Avg. Silence (s) ↓ |
|---|---|---|---|---|---|---|---|
| Gemini-2.5 Flash | **0.76 ± 0.04** | 0.70 ± 0.05 | **0.64 ± 0.06** | **5.00 ± 3.07** | 8.92 ± 5.62 | 0.74 ± 0.07 | **6.63 ± 2.16** |
| Qwen-2-VL-Max | 0.59 ± 0.05 | 0.71 ± 0.04 | 0.59 ± 0.06 | 6.92 ± 3.91 | 46.53 ± 11.16 | **0.94 ± 0.15** | 8.81 ± 2.78 |
| GPT-4.1 mini | 0.52 ± 0.05 | 0.70 ± 0.05 | 0.58 ± 0.06 | 65.28 ± 18.59 | 21.46 ± 4.77 | 1.48 ± 0.46 | 19.37 ± 5.89 |
| Claude 4 | 0.51 ± 0.05 | 0.69 ± 0.05 | 0.56 ± 0.05 | 19.83 ± 4.39 | **7.45 ± 2.14** | 0.62 ± 0.06 | 18.57 ± 8.78 |
| Claude 4.5 | 0.59 ± 0.05 | 0.70 ± 0.04 | 0.61 ± 0.05 | 20.50 ± 4.30 | 12.86 ± 3.31 | 0.70 ± 0.07 | 10.40 ± 5.86 |
| GPT-5.2 | 0.55 ± 0.05 | **0.73 ± 0.04** | 0.63 ± 0.05 | 22.56 ± 4.76 | 11.14 ± 2.74 | 0.65 ± 0.07 | 14.85 ± 7.31 |

<details>
<summary>VideoA11y-Ref scores (1–5)</summary>

| Model | Descriptive ↑ | Objective ↑ | Accurate ↑ | Clear ↑ |
|---|---|---|---|---|
| Gemini-2.5 Flash | 3.10 ± 0.19 | 2.92 ± 0.22 | **1.63 ± 0.15** | **2.69 ± 0.20** |
| Qwen-2-VL-Max | 2.58 ± 0.22 | 2.77 ± 0.25 | 1.33 ± 0.14 | 2.21 ± 0.21 |
| GPT-4.1 mini | **3.28 ± 0.24** | 2.82 ± 0.22 | 1.31 ± 0.14 | 2.19 ± 0.23 |
| Claude 4 | 2.32 ± 0.18 | 2.13 ± 0.20 | 1.16 ± 0.10 | 2.01 ± 0.18 |
| Claude 4.5 | 2.69 ± 0.21 | 2.77 ± 0.24 | 1.26 ± 0.13 | 2.21 ± 0.22 |
| GPT-5.2 | 2.89 ± 0.21 | **2.94 ± 0.26** | 1.35 ± 0.14 | 2.54 ± 0.25 |

</details>

### Next-generation omni-modal models (Gd. + Timing ASR prompt; paper Tab. 6)

Each model is scored on all test videos it has output for: 101 for Gemini-2.5/3.5 Flash and Qwen-2-VL-Max, 99 for
Qwen-3.5-Omni-Plus, and 57 for Qwen-3.5-Omni-Flash (after the scoring exclusions).

| Model | Prioritization ↑ | No Redundancy ↑ | On-Screen Text ↑ | Timeliness (s) ↓ | Overlaps w. TTS (s) ↓ | Audio Coverage (≤1) | Avg. Silence (s) ↓ |
|---|---|---|---|---|---|---|---|
| Gemini-2.5 Flash | 0.76 ± 0.04 | 0.70 ± 0.05 | **0.64 ± 0.06** | 5.00 ± 3.07 | 8.92 ± 5.62 | 0.74 ± 0.07 | 6.63 ± 2.16 |
| Gemini-3.5 Flash† | 0.93 ± 0.03 | **0.90 ± 0.03** | 0.57 ± 0.05 | 2.99 ± 0.91 | **0.26 ± 0.13** | 0.45 ± 0.03 | **4.85 ± 1.20** |
| Qwen-2-VL-Max | 0.59 ± 0.05 | 0.71 ± 0.04 | 0.59 ± 0.06 | 6.92 ± 3.91 | 46.53 ± 11.16 | **0.94 ± 0.15** | 8.81 ± 2.78 |
| Qwen-3.5-Omni-Flash† | **0.94 ± 0.02** | 0.88 ± 0.05 | 0.61 ± 0.06 | 4.91 ± 3.64 | 52.14 ± 15.89 | **0.94 ± 0.20** | 16.61 ± 11.88 |
| Qwen-3.5-Omni-Plus† | 0.79 ± 0.06 | 0.88 ± 0.04 | 0.58 ± 0.06 | **2.67 ± 1.04** | 30.45 ± 8.69 | 0.72 ± 0.14 | 9.49 ± 2.12 |

<details>
<summary>VideoA11y-Ref scores (1–5)</summary>

| Model | Descriptive ↑ | Objective ↑ | Accurate ↑ | Clear ↑ |
|---|---|---|---|---|
| Gemini-2.5 Flash | 3.10 ± 0.19 | 2.92 ± 0.22 | 1.63 ± 0.15 | 2.69 ± 0.20 |
| Gemini-3.5 Flash† | 4.02 ± 0.15 | 4.07 ± 0.15 | 3.79 ± 0.18 | 4.02 ± 0.17 |
| Qwen-2-VL-Max | 2.58 ± 0.22 | 2.77 ± 0.25 | 1.33 ± 0.14 | 2.21 ± 0.21 |
| Qwen-3.5-Omni-Flash† | **4.43 ± 0.23** | **4.41 ± 0.20** | **3.98 ± 0.25** | **4.27 ± 0.26** |
| Qwen-3.5-Omni-Plus† | 3.82 ± 0.27 | 4.03 ± 0.26 | 3.23 ± 0.25 | 3.81 ± 0.27 |

</details>

† LLM-judged metrics (Prioritization, No Redundancy, Timeliness, VideoA11y-Ref) for these models use Gemini-3.5 Flash
as the judge (the other models use Gemini-2.5 Flash). The segment-level metrics appear robust to this change, but
VideoA11y-Ref values are not directly comparable across the two groups. Programmatic metrics (On-Screen Text, Overlaps,
Coverage, Avg. Silence) do not depend on the judge.

Tab. 5 and Tab. 6 (and the paper's model comparison figures) are recomputed exactly from the released per-video
metrics with `analysis/paper_tables_and_figures.ipynb` in the [code release](https://github.com/google/mtg-ad).

## How to Use

### Download the original videos
The dataset contains YouTube video IDs, not video files. Videos can be downloaded with `yt-dlp`, subject to YouTube's
Terms of Service and video availability:

```bash
pip install yt-dlp
while IFS= read -r vid_id; do
  yt-dlp "https://www.youtube.com/watch?v=$vid_id" -o "videos/%(id)s.%(ext)s"
done < test_video_ids.txt
```

### Work with ground-truth segments
```python
from datasets import load_dataset

test = load_dataset("google/mind-the-gap-inline-ad", "annotations", split="test")
row = test[0]
for seg in row["gt_ad_segments"]:
    print(seg["start_seconds"], seg["text"])
```

## Limitations

- **Scale:** 433 videos is a modest scale; results may not generalize to all video styles.
- **Language:** all videos and annotations are in English.
- **Domain:** YouTube user-generated content; may not generalize to professional broadcast media.
- **Visual information:** generated by multimodal models and may reflect their biases and errors; 25 validation videos
  use a different model (Gemini 3.7 Flash) than the rest, and 2 validation videos have none.
- **ASR transcripts:** automatic and may contain recognition errors; 114 videos have empty transcripts (no transcribed
  speech).
- **Video availability:** videos are hosted on YouTube and may become unavailable over time (link rot).
- **Metric coverage:** automated metrics evaluate a prioritized subset of guidelines (G1.1–G1.3, G2, G7, G8); aspects
  such as active voice (G5) and prosody (G10) are not measured programmatically.

## Ethical Considerations

**Personal and sensitive information.** The videos show real people in public user-generated YouTube content. No
personally identifiable information beyond what is publicly available in the original videos is included. AD
annotations may describe people visible in the frames. People appearing in the videos may not have specifically
consented to research use of their likeness.

**Annotators.** Annotations were produced by professional AD teams of sighted and blind writers. Annotator demographic
information is not released to protect privacy.

**Social impact.** The dataset supports video accessibility research for blind and low-vision (BLV) audiences by
providing a high-quality benchmark and actionable evaluation for automated inline audio description.

## Citation

```bibtex
@inproceedings{venugopalan2026mindthegap,
  title     = {Mind The Gap ({MTG-AD}): Dataset and Fine-grained Evaluation for End-to-End Inline Audio Descriptions},
  author    = {Venugopalan, Subhashini and Tan, Jenny and Roper, Taylor and Tobin, Jimmy and
               Kast, Anton and Martin, Alicia and Sepah, Sam and Pavel, Amy},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track},
  year      = {2026}
}
```

## License

The annotations in this dataset are released under the
[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) license.
The original videos are not redistributed and remain subject to their owners' rights and YouTube's Terms of Service.

**Creator:** Google LLC