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dataset_info:
features:
- name: meeting_id
dtype: string
- name: abstractive
struct:
- name: abstract
list: string
- name: actions
list: string
- name: decisions
list: string
- name: problems
list: string
- name: participants_summary
struct:
- name: A
struct:
- name: abstract
list: string
- name: actions
list: string
- name: decisions
list: string
- name: problems
list: string
- name: B
struct:
- name: abstract
list: string
- name: actions
list: string
- name: decisions
list: string
- name: problems
list: string
- name: C
struct:
- name: abstract
list: string
- name: actions
list: string
- name: decisions
list: string
- name: problems
list: string
- name: D
struct:
- name: abstract
list: string
- name: actions
list: string
- name: decisions
list: string
- name: problems
list: string
- name: topics
list: string
- name: key_speakers
list:
- name: speaker_id
dtype: string
- name: speech
dtype: string
- name: name_entities
list:
- name: text
dtype: string
- name: type
dtype: string
- name: utterance
list:
- name: speaker
dtype: string
- name: start_time
dtype: float64
- name: end_time
dtype: float64
- name: text
dtype: string
splits:
- name: train
num_bytes: 9648986
num_examples: 139
download_size: 9666002
dataset_size: 9648986
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for AMI_annotation
## Dataset Details
### Dataset Description
**AMI_annotation** is a structurally parsed and annotated multi-modal meeting corpus derived from the AMI Meeting Corpus manual annotations. It features detailed meeting-level metadata, abstractive meeting summaries, individual participant summaries mapped by roles/speakers, segmented timestamps, speaker-specific text transcriptions, integrated Named Entity Recognition (NER), and discussion topic vectors.
- **Curated by:** Rabindra Nath Nandi
- **Language(s) (NLP):** English
- **License:** [More Information Needed]
## Dataset Structure
### Data Instances
Each row in the dataset represents a unique meeting instance indexed by its `meeting_id`.
### Data Fields
* `meeting_id` (string): Unique identifier for the meeting session (e.g., `ES2002a`).
* `abstractive` (struct): Meeting-level abstractive summaries containing:
* `abstract` (list of strings): High-level meeting summary sentences.
* `actions` (list of strings): Stated action items and directives.
* `decisions` (list of strings): Concluded decisions made by the team.
* `problems` (list of strings): Open issues or problems discussed.
* `participants_summary` (struct): Role-specific summaries for participants (`A`, `B`, `C`, `D`), tracking individual perspectives on abstract, actions, decisions, and problems.
* `topics` (list of strings): Chronological meeting agenda and discussion topics.
* `key_speakers` (list): Speaker profile records containing:
* `speaker_id` (string): Identifier code for the speaker.
* `speech` (string): Consolidated full-text monologue or contributions.
* `name_entities` (list): Extracted entities mapped to types (`text`, `type`).
* `utterance` (list): Time-aligned utterance blocks containing:
* `speaker` (string): Speaker label.
* `start_time` (float64): Utterance start timestamp in seconds.
* `end_time` (float64): Utterance end timestamp in seconds.
* `text` (string): Transcribed text segment.
## Uses
### Direct Use
Designed for training and evaluating Audio Large Language Models (AudioLLMs), automatic meeting summarization, multi-party dialogue analysis, and named entity recognition tasks.
## Dataset Creation
### Curation Rationale
Processed using automated XML parsers to structure legacy AMI corpus annotations into clean, standardized JSON table rows compatible with the Hugging Face `datasets` ecosystem.
## Bias, Risks, and Limitations
Corpus reflects natural multi-party meeting dynamics, containing colloquial speech patterns, interruptions, and subjective participant summaries. |