tuanhqv123/meeting-summarization

Dialogue Summarization Dataset

Short conversation → abstractive summary pairs, used to fine-tune tuanhqv123/longt5-meeting-summarization.

A cleaned merge of two public dialogue-summarization benchmarks: DialogSum and SAMSum.

Fields

Field	Type	Description
input	string	The dialogue / conversation
output	string	Reference abstractive summary
source	string	dialogsum or samsum

Splits

Each source's original train/val/test split is preserved (no re-shuffling across splits, so the splits stay comparable to published DialogSum/SAMSum results). The combined ratio is ~88 / 4 / 8:

Split	Total	%	dialogsum	samsum
train	26,095	88.1%	12,453	13,642
validation	1,256	4.2%	500	756
test	2,259	7.6%	1,489	770

Conversations are short: input median ≈ 73–116 words, p95 ≈ 400 tokens — no long context needed.

Cleaning

Built from the raw datasets with a discovery-driven pipeline (see the training repo):

• normalize unicode quotes/dashes, strip emojis (SAMSum chat noise)
• drop auto-detected disfluencies (uh, um, hmm…)
• unify speaker tags: DialogSum's #Person1# → Person1, matching SAMSum's bare-name style
• filter: input ≥ 20 words, output 5–200 words, drop exact duplicates

Kept ~96% of rows (30,828 → 29,610).

Sources & licenses

Both are public benchmark datasets (no proprietary/internal data):

source tag	train rows	origin	license
dialogsum	12,453	DialogSum — two-person dialogues with #PersonN# tags	MIT
samsum	13,642	SAMSum — messenger-style group chats	CC-BY-NC-ND 4.0

Please respect the original licenses. Cite the original works when using this data.

Usage

from datasets import load_dataset

ds = load_dataset("tuanhqv123/meeting-summarization")
print(ds)
print(ds["train"][0]["input"][:300])
print(ds["train"][0]["output"])