Auto-highlight meeting tasks in transcripts

Automatically highlighting meeting tasks in transcripts involves identifying and tagging actionable items such as tasks, assignments, deadlines, or follow-ups mentioned during a conversation. Here’s a clear explanation of how to achieve that using a combination of natural language processing (NLP) techniques and rule-based logic:

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1. Define What a “Task” Is

Before automation, it’s essential to outline what constitutes a task:

• Action-oriented sentences (e.g., “We need to finish the report by Monday.”)

• Assigned responsibility (e.g., “John will email the client.”)

• Due dates or time references (e.g., “by the end of this week”)

• Modal verbs and action verbs (e.g., “should”, “must”, “will”, “need to”)

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2. Transcript Preprocessing

Clean the transcript text:

• Remove filler words (“um”, “uh”, etc.)

• Normalize punctuation and casing

• Segment the transcript into individual speaker turns or sentences

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3. Task Detection Techniques

A. Rule-Based Approach (Lightweight & Fast)
Use regular expressions and keyword lists:

• Look for patterns like:

  • "[Name] will [verb]", e.g., “Alice will send the agenda”

  • "We need to [verb]", e.g., “We need to update the pricing sheet”

  • "Let's [verb]", e.g., “Let’s finalize the presentation”

• Identify date/time phrases using libraries like dateparser or spaCy’s EntityRecognizer for DATE, TIME

B. NLP-Based Approach (More Accurate)
Use NLP models to extract tasks:

• Named Entity Recognition (NER) to detect assignees and deadlines

• Dependency parsing to extract subject-verb-object structures

• Transformers or fine-tuned models (e.g., BERT, GPT) to identify task-oriented language

Example using spaCy:

python
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import spacy

nlp = spacy.load("en_core_web_sm")
text = "John will prepare the slides for next week’s meeting."

doc = nlp(text)
for sent in doc.sents:
    if any(tok.lemma_ in ["need", "should", "must", "will"] for tok in sent):
        print("Task detected:", sent.text)

C. Use of Pre-trained Models
Use models trained on meeting data (like those in the MeetingBank dataset) or fine-tuned T5/BART models that extract action items.

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4. Highlight Tasks in Transcript

Once tasks are detected, highlight them with simple HTML or markdown tags in your UI:

html
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<p>John will <mark>prepare the slides</mark> for next week’s meeting.</p>

Or annotate them in plain text:

vbnet
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TASK: John will prepare the slides for next week’s meeting.

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5. Optional: Structure Extracted Tasks

Convert extracted tasks into structured data:

json
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{
  "task": "prepare the slides",
  "assignee": "John",
  "due": "next week’s meeting"
}

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6. Tools and Libraries to Use

• spaCy or NLTK: for NLP tasks

• transformers (HuggingFace): for advanced model-based detection

• dateparser / duckling: for parsing due dates

• regex + custom logic: for fast rule-based identification

• ASR providers like Otter.ai, Zoom, Google Meet: for input transcripts

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7. Integration Tip
You can integrate this into a workflow by:

• Running the transcript through your task detection pipeline post-meeting

• Displaying tasks separately in a dashboard

• Sending follow-ups via email or notifications with detected tasks

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This automated system can dramatically reduce the effort needed to manage meeting action items and ensure follow-through on responsibilities.