LLMs for extracting actionable items from meeting notes

Large Language Models (LLMs) like GPT-4 and others have proven highly effective in parsing and understanding meeting notes. They can be leveraged to extract actionable items, which is crucial for improving productivity and ensuring that important tasks are clearly identified and followed through. Here’s how LLMs can be utilized for extracting actionable items from meeting notes:

1. Understanding the Context of the Meeting

LLMs can process the meeting notes in their entirety, identifying key topics, issues discussed, and the overall context. By understanding the flow of the conversation, an LLM can distinguish between general discussion points and those that involve decisions or tasks.

For instance, a sentence like “We need to follow up on the budget approval next week” would be recognized as an actionable item, while a broader discussion about budget challenges would not.

2. Identifying Key Actionable Phrases

LLMs are adept at recognizing phrases that suggest action is required. These phrases include:

• “Follow up”

• “Action required”

• “Assigned to”

• “Due by”

• “Schedule”

• “Prepare”

• “Review”

• “Send”

• “Finalize”

An LLM can be trained or prompted to focus on these keywords and extract the sentences or parts of the notes that imply future actions. For example:

• “John will send the project update by Friday.”

• “Sarah will schedule a follow-up meeting for next Tuesday.”

These statements contain specific individuals and clear actions, making them actionable items.

3. Summarizing Tasks with Assigned Responsibilities

One of the most valuable uses of LLMs in this context is their ability to extract both the action and the person responsible for it. LLMs can match names and tasks efficiently, even when they are embedded in complex or lengthy sentences.

For example, if meeting notes include a sentence like:

• “Michael is responsible for reviewing the proposal before the next team call.”

The LLM can extract this as an actionable item assigned to Michael with a specific task of reviewing the proposal.

4. Tracking Deadlines and Timelines

Deadlines are a critical component of actionable items. LLMs can be designed to recognize temporal expressions such as:

• “By Friday”

• “Next week”

• “End of the month”

• “ASAP”

They can then link the deadlines to the associated tasks. For example, if a note says, “Alice needs to finalize the report by Friday,” the LLM will flag both the task (finalizing the report) and the deadline (Friday).

5. Categorizing Action Items

LLMs can categorize tasks based on predefined labels, such as:

• Follow-up Tasks

• Decisions Needed

• Meetings to Schedule

• Documents to Review

By analyzing the content of the notes, the LLM can sort each actionable item into a category, allowing for easier task management and follow-up.

6. Extracting Actionable Items from Unstructured Notes

Meeting notes are often informal, fragmented, and may not follow a structured format. LLMs excel at interpreting unstructured data and converting it into structured information. They can be instructed to parse notes, even if they lack clear headings or bullet points, to identify actionable items.

For example:

• “We discussed increasing the marketing budget for Q3. Let’s aim for a 10% increase.”

  • Actionable Item: Increase marketing budget by 10% for Q3.

• “Bob suggested we start a new project on AI.”

  • Actionable Item: Start new project on AI, as proposed by Bob.

7. Integrating with Task Management Systems

Once the actionable items have been identified, LLMs can integrate with task management systems like Trello, Asana, or Jira to automatically create tasks or reminders based on the extracted information. This can save time and ensure that no tasks are overlooked.

For example, after extracting an actionable item like “John to send the report by Friday,” the LLM can generate a task in a project management tool with the task name, deadline, and assignee.

8. Monitoring Action Item Progress

LLMs can track the status of these actionable items over time, especially when used in conjunction with a project management tool. They can flag overdue tasks, follow up on pending actions, or send reminders based on deadlines. This ensures accountability and keeps the project on track.

For example, if an action item has a due date, the LLM can scan the calendar and notify the relevant parties about any delays.

9. Ensuring Accuracy in Task Extraction

LLMs can be fine-tuned to understand different styles of meeting notes or corporate vernacular. Custom training can improve their accuracy in extracting action items from specific industries or meeting formats. For example, in tech meetings, they might be better at identifying code review tasks, while in marketing meetings, they may focus on content development tasks.

10. Automating Reports on Actionable Items

LLMs can also generate summary reports of all actionable items extracted from multiple meeting notes. These reports can be generated automatically and sent to the team, helping stakeholders stay informed of progress.

For example, after several meetings, an LLM could compile a daily or weekly report that lists all tasks, deadlines, and responsible individuals in a structured format.

Conclusion

Leveraging LLMs for extracting actionable items from meeting notes can streamline operations, enhance accountability, and reduce the risk of overlooking important tasks. By integrating advanced natural language understanding with task management tools, businesses can significantly improve their workflow, ensuring that decisions made in meetings translate into clear actions.