AugmendTech/hseg

This repo makes two contributions:

1. Code for extracting utterances and ground truth segmentation from ICSI and AMI.
2. Adapted versions of two unuspervised meetings segmentation approaches.

ICSI and AMI datasets:

AMI:

• Download the AMI corpus from here: https://groups.inf.ed.ac.uk/ami/AMICorpusAnnotations/ami_public_manual_1.6.2.zip
• Create a folder named AMI in the datasets dir (datasets/AMI/) and extract it there.

ICSI:

• Download the ICSI corpus from here: https://groups.inf.ed.ac.uk/ami/ICSICorpusAnnotations/ICSI_plus_NXT.zip
• Create a folder named ICSI in the datasets dir (datasets/ICSI/) and extract it there.

The two datasets are available via the classes ICSIDataset and AMIDataset

Baselines

Both baselines are adapted to receive a desired number of segments K. The top K-1 splitting points are used to segment the meeting into K segments.

Unsupervised Topic Segmentation of Meetings with BERT Embeddings

• Paper: https://arxiv.org/abs/2106.12978
• Original repo: https://github.com/gdamaskinos/unsupervised_topic_segmentation

The code is adapted from the original repo and differs in two major ways:

• It uses OpenAI instead of Roberta embeddings.
• In the original repo the ICSI/AMI datasets were assumed to reside in some database and were thus unavailable. We have adapted the code to use our version of ICSI and AMI as described above.

Unsupervised Dialogue Topic Segmentation in Hyperdimensional Space (Interspeech 2023)

• Paper: https://arxiv.org/abs/2308.10464
• Original repo: https://github.com/seongminp/hyperseg

The original repo did not contain extracted data for ICSI/AMI. We have adapted the code to our version of ICSI and AMI as described above.

Running

• The entry point is main.py

• You can invoke it as follows python3 main.py --model MODEL_NAME --dataset DATASET_NAME --mid MEETING_ID

• model: bertseg (BERT embeddings), hyperseg (hyperdimensional vectors), random (random segmentation), equi (equidistant segments), view (will print meeting in-order, segment by segment)

• dataset: icsi or ami

• mid: 0-indexed meeting id as an integer (e.g. 1 will evaluate on the second meeting in the corpus)

• Only the first 5 meetings from each corpus are loaded to save time testing. To remove this restriction pass restricted=False to the dataset class constructor.

• For bertseg you will need to set up an environment variable named OPENAI_API_KEY with your OpenAI key, so that the embeddings can be extracted. Calls to the embeddings endpoint are inexpensive. We are using the text-embedding-3-large model. For more information on pricing, take a look here: https://openai.com/api/pricing/

• The original segment transitions are pruned so that segments have a minimum size of 10 utterances. In the end a plot is displayed comparing the original, pruned and inferred segment transitions points.