Open
Description
The objective of the CL is to continuously learn and improve the experience of the summaries based on the set of meetings that goes through a channel/group.
The Enrichment process happens for 2 components.
- Channel Model.
- Channel Mind.
Channel model contains files that are GPT related (helps to represent information based on the channel context) and Channel mind contains a set of files that keeps track of the importance of discussions that are discussed in the channel.
Each Channel has its own customized mind, whereas the mind can be common to more than one channel.
The Channel Mind contains the below artifacts
- keyphrase entity graph: A structured representation of all the key phrases and entities that are important to the channel.
- Entity Features: A dictionary of important entities and it's respective feature vector.
- Entity community mapping: Discussions are clubbed together based on the entity's context.
- Community global context ranking: Importance of community, based on all the meetings that have happened in the respective channel.
- Community local context ranking: Importance of community, based on recent (5) meetings that have happened in the respective channel.
The Algorithm involves the below:
- Extract groups from the meeting segments.
- For each highlight, extract key phrases and extend it to entities using the keyphrase entity graph.
- Using the extended entites, find the most similar entities w.r.t the highlights.
- find which communities the entities belong to, for clubbing other entities based on similar discussion.
- Find Community Agreeableness for a particular highlight based on the selected community mapping.
- Check if the community is present in Local context ranking, if it does, use it's ranking else check in global context ranking. Local context ranking (recency) would always be given higher priority than the global context.
- Update the local context, global context with freq of communities picked during that call and enrich keyphrase entity graph with entities and keyphrases.
To Do:
- A new service needs to be created which would update all the artifacts after the respective call ends.
- Segment Analyser service needs to be updated with gc, lc changes.
- Managing S3 artifacts need to be done via API service. ie. copying artifacts when there is a new mind selected from a group.
- Structure of segment analyser service response that is given to API service needs to be changed.
- API service needs to call new lambda service to handle mind artifacts updation.< 5658 /li>