metadata

language:
  - en
size_categories:
  - 1K<n<10K
task_categories:
  - summarization
pretty_name: >-
  TIB: A Dataset for Abstractive Summarization of Long Multimodal
  Videoconference Records
dataset_info:
  features:
    - name: doi
      dtype: string
    - name: title
      dtype: string
    - name: url
      dtype: string
    - name: video_url
      dtype: string
    - name: license
      dtype: string
    - name: subject
      dtype: string
    - name: genre
      dtype: string
    - name: release_year
      dtype: string
    - name: author
      dtype: string
    - name: contributors
      dtype: string
    - name: abstract
      dtype: string
    - name: transcript
      dtype: string
    - name: transcript_segments
      sequence:
        - name: id
          dtype: int32
        - name: seek
          dtype: int32
        - name: start
          dtype: float32
        - name: end
          dtype: float32
        - name: text
          dtype: string
        - name: tokens
          sequence: int32
        - name: temperature
          dtype: float32
        - name: avg_logprob
          dtype: float32
        - name: compression_ratio
          dtype: float32
        - name: no_speech_prob
          dtype: float32
    - name: keyframes
      sequence:
        - name: slide
          dtype: string
        - name: frames
          sequence: int32
        - name: timestamp
          sequence: float32
    - name: language
      dtype: string
  splits:
    - name: train
      num_bytes: 827419303
      num_examples: 7282
    - name: test
      num_bytes: 102381848
      num_examples: 911
    - name: valid
      num_bytes: 101368222
      num_examples: 910
  download_size: 501919138
  dataset_size: 1031169373
pinned: true
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
      - split: valid
        path: data/valid-*

Dataset Card for "TIB: A Dataset for Abstractive Summarization of Long Multimodal Videoconference Records"

More Information needed

Dataset Description

Homepage: Dataset page
Repository: Dataset page
Paper: TIB: A Dataset for Abstractive Summarization of Long Multimodal Videoconference Records
Point of Contact: Théo Gigant

Dataset Summary

TIB is an English dataset for abstractive summarization of multimodal presentations, introduced in TIB: A Dataset for Abstractive Summarization of Long Multimodal Videoconference Records . It is a collection of 9,103 videoconference records extracted from the German National Library of Science and Technology (TIB) archive, along with their metadata, an abstract and automatically processed transcripts and key frames.

Supported Tasks and Leaderboards

summarization

Languages

The text in the dataset is in English, both for the transcripted audios and the abstracts.

Usage

To use within the datasets library:

from datasets import load_dataset

dataset = load_dataset("gigant/tib")

Dataset Structure

Data Instances

A typical data point represents a videoconference record, the transcript and keyframes are textual and visual modalities, processed from the video found at video_url, and the abstract is used as a target abstractive summary.

Data Fields

Each record consist of the following attributes:

doi: digital object identifier (DOI) of the record or the associated paper
title: title of the presentation
url: URL of the record in the TIB archive
video_url: URL of the video file
license: license of the record
subject: academic field (eg Computer Science, Mathematics, ...)
genre: type of presentation (eg Lecture, Conference, ...)
release_year: year the record was released
author: name of the author
contributors: name of the contributors
abstract: the abstract of the presentation, that serve as a target summary
transcript: the automatically extracted transcript
transcript_segments: the automatically extracted transcript with time codes, output of the speech recognition system
keyframes: the automatically extracted key frames time codes

doi, title, url, video_url, license, subject, genre, release_year, author, contributors and abstract are provided as found in the TIB archive. The length, style, quality and content of the abstract can differ from video to video as it was likely provided by each author. For instance, some abstracts can provide very short title-like summaries, introduction of the conference, the lecture or the speaker, or longer descriptions of the content. We provide examples of transcripts and summaries in the paper's Appendix.

Data Splits

The data is split into a training, validation and test set.

Train: 7,282 (80%)
Validation: 910 (10%)
Test: 911 (10%)

Dataset Creation

Source Data

Initial Data Collection and Normalization

The dataset was first assembled by crawling the TIB-AV portal which is a large archive of videos, developed by the German National Library of Science and Technology: Technische Informationsbibliothek (TIB). Entries with missing abstracts or abstracts that were too short (less than 30 characters) were filtered out. We also filtered out records for which the abstract or the transcript is in another language than English. In order to keep the abstracts that are relevant to the associated record, we removed documents if the abstract is the same as the abstract for another video. This allowed to get rid of all the abstracts that were written for a set of records such as conferences, instead of specifically written for a single presentation.

More information about the dataset collection and filtering can be found in TIB: A Dataset for Abstractive Summarization of Long Multimodal Videoconference Records .

Dataset Curators

The dataset was initially created by Théo Gigant, Frédéric Dufaux, Camille Guinaudeau and Marc Decombas.

Citation Information

@inproceedings{gigant:hal-04168911,
  TITLE = {{TIB: A Dataset for Abstractive Summarization of Long Multimodal Videoconference Records}},
  AUTHOR = {GIGANT, Th{\'e}o and Dufaux, Fr{\'e}d{\'e}ric and Guinaudeau, Camille and Decombas, Marc},
  URL = {https://hal.science/hal-04168911},
  BOOKTITLE = {{Proc. 20th International Conference on Content-based Multimedia Indexing (CBMI 2023)}},
  ADDRESS = {Orl{\'e}ans, France},
  ORGANIZATION = {{ACM}},
  YEAR = {2023},
  MONTH = Sep,
  KEYWORDS = {multimedia dataset, multimodal documents, automatic summarization},
  HAL_ID = {hal-04168911},
  HAL_VERSION = {v1},
}