A corpus of public meetings
public_meetings: a meeting corpus of aligned pairs of transcriptions and reports.
paper review ongoing, contact firstname.lastname@example.org for more information
see also https://github.com/pltrdy/autoalign
- from pip:
pip install public_meetings
- from sources:
git clone https://github.com/pltrdy/public_meetings.git cd public_meetings pip install .
This corpus contains meetings, made of pairs of (a) automatic transcriptions from audio recordings, (b) meeting reports written by a professional.
Both texts are way too long to be reasonibly processed (e.g. by neural models) so we worked on the automatic segmentation and alignment to get suitable pairs for meeting summarization training/evaluation.
We present a public extract of our data in this repository. The segmentation/alignment can be found at https://github.com/pltrdy/autoalign.
Reading the data
We provide 22 aligned meetings that can be loaded easily:
import public_meetings meetings = public_meetings.load_meetings()
Meetings are identified by a hash e.g.:
meeting = meetings['81540075987931464031780e046c0d8f']
Each meetings has been automatically aligned first
meeting['initial'] then post-edited by a human annotator,
meeting['final']. Each alignment has a transcription (aka.
ctm) and a report side (aka.
doc) that contains segments (usually several sentences).
meeting['final']['doc'][i]['text'] # text of the i-th document segment meeting['final']['doc'][i]['id'] # id of the i-th document segment meeting['final']['ctm'][j]['text'] # text of the j-th transcription segment meeting['final']['ctm'][j]['id'] # id of the j-th transcription segment meeting['final']['ctm'][j]['aligned'] # doc segment id corresponding to the j-th transcription segment
public_meetings provides utility functions from a single entry point:
Commands are listed in the following sections.
prepare: process all meetings to src/tgt files.
prepare command is meant to prepare meetings for summarization models (either for training or inference).
It basically load every meetings and write the transcription side in a
[prefix].src.txt file and the report side in a
[prefix].tgt.txt. Many parameters can be set to filter segments, on their number of words/sentences (both min and max values).
Example from the paper:
./prepare.py \ -mw 10 -Mw 1000 \ -ms 3 -Ms 50 \ -overlap_prct 0 -n_draw 0 \ -remove_unk \ -sentence_tag \ -remove_names \ -remove_headers \ -remove_p
public_meetings prepare -h usage: prepare [-h] [-dir DIR] [-mw MW] [-Mw MW] [-ms MS] [-Ms MS] [-remove_tags] [-remove_unks] [-remove_names] [-remove_headers] [-remove_p] [-sentence_tags] [-overlap_prct OVERLAP_PRCT] [-n_draw N_DRAW] [-output OUTPUT] [-verbose] optional arguments: -h, --help show this help message and exit -dir DIR, -d DIR Aligned meeting root -mw MW Min #words -Mw MW Max #words -ms MS Min #sentences -Ms MS Max #sentences -remove_tags Remove every tags i.e. <*> -remove_unks Remove unknown tags i.e. <unk> -remove_names Remove names i.e. <nom>*</nom> -remove_headers Remove headers i.e. <h>*</h> -remove_p Remove paragraph tags i.e. <p> and </p> -sentence_tags And sentence separators <t> and </t> -overlap_prct OVERLAP_PRCT, -oprct OVERLAP_PRCT -n_draw N_DRAW -output OUTPUT Output path prefix -verbose, -v
segmentation: process the transcription side in a linear segmentation fashion.
We use this before running linear segmentation experiments. It only considers transcription side of meetings, and write it to source (one segment per line) and reference (one segment per line + segmentation separator
You just have to set an
output_root directory to recieve the text files, and, optionnally a different
public_meetings segmentation -o ./public_meetings_txt
public_meetings segmentation -h usage: segmentation [-h] [-meeting_root MEETING_ROOT] -output_root OUTPUT_ROOT optional arguments: -h, --help show this help message and exit -meeting_root MEETING_ROOT, -m MEETING_ROOT Meeting root directory -output_root OUTPUT_ROOT, -o OUTPUT_ROOT Output root directory
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