Datasets:

AmyIvan
/

mosaic-acl2026

pretty_name: MOSAIC
license: cc-by-nc-sa-4.0
language:
  - zh
  - en
task_categories:
  - summarization
  - text-generation
  - feature-extraction
size_categories:
  - 10K<n<100K
tags:
  - education
  - multimodal
  - subtitles
  - knowledge-graph
  - slides

MOSAIC

Dataset Summary

MOSAIC is a course-centric multimodal dataset released with an ACL 2026 paper. The dataset centers on mosaic.jsonl, a JSONL file that stores course-level metadata together with nested video-level summaries, subtitles, captions, and auxiliary references.

The dataset also includes:

data/graph_p_results/: course-level knowledge graph JSON files keyed by kg
data/all.csv: URL-to-filename mapping for slide references
data/pdfs/shard_xx/: sharded reference slide PDFs

Supported Tasks

multimodal educational data understanding
subtitle and caption analysis
document-aware summarization
course knowledge graph grounding
retrieval over linked videos, graphs, and slides

Languages

The dataset is primarily in Chinese, with a smaller amount of English content in slide titles, references, and course materials.

Dataset Structure

.
├── README.md
└── data/
    ├── mosaic.jsonl
    ├── all.csv
    ├── graph_p_results/
    │   ├── BIT-1001604004.json
    │   └── ...
    └── pdfs/
        ├── shard_00/
        ├── shard_01/
        └── ...

Data Instances

Main file: `data/mosaic.jsonl`

Each line is one course record with the following top-level fields:

url
course_title
contents
kg
caption_anno
overview
objectives
prerequisites
references

Each video entry inside contents[*].courses[*] contains:

video_url
srt_url
summary
subtitle
caption
video_title
ref

The ref object includes:

cate: reference category
doc: list of reference document URLs

Knowledge graphs: `data/graph_p_results/*.json`

Each knowledge graph file contains a top-level object with:

code
message
sampled
traceId
result

The main graph payload is stored in:

result.mocKgNodeDtoList

Slide mapping: `data/all.csv`

Columns:

doc_url: document URL referenced in mosaic.jsonl
filename: corresponding PDF filename

PDFs: `data/pdfs/shard_xx/`

Reference slide PDFs are sharded into directories of up to 500 files each for more reliable upload and browsing.

Dataset Creation

MOSAIC is constructed from public courses on iCourse163, a major Chinese MOOC platform. The source data follows a four-level hierarchy of course, chapter, video, and topic. Each course provides course-level metadata such as objectives and prerequisite information; chapters group related videos and associated slide decks; videos include timestamped ASR transcripts, instructor-provided knowledge-point outlines, and summaries; and topics correspond to the predefined knowledge points used for alignment. Because the platform does not provide high-quality alignment between transcripts, topic inventories, and slides, the dataset constructs these links from scratch. MOSAIC is released in two subsets: MOSAIC-G, a fully human-annotated gold benchmark built from 6 diverse courses with utterance-level topic labels and utterance-to-slide alignment, and MOSAIC-S, a large silver subset for the remaining courses produced with DORA, a two-stage pipeline that first refines noisy topic inventories and then performs joint segmentation and topic assignment. For slide linkage in MOSAIC-S, the paper describes an automatic pipeline combining title matching, rule-based filtering, and LLM verification.

Statistics

Metric	Value
Courses	179
Videos	14,942
Knowledge graph JSON files	167
PDF files	10,566
Slide mapping rows	10,566
Raw size	~12.17 GB (11.34 GiB)

Licensing Information

This dataset is released under CC BY-NC-SA 4.0.

Citation Information

@inproceedings{ai-etal-2026-mosaic,
  title = {MOSAIC: A Large-Scale Multimodal Open-Course Segmentation and Alignment Corpus in Chinese},
  author = {Ai, Yuming and Fan, Shuai and Xu, Hua and Kong, Fang},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics},
  year = {2026}
}