README.md

metadata

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.