Meeting Summarization Use Case

[manisnesan]

          [From rasbt post](https://x.com/rasbt/status/1754516687896887449?s=46&t=aOEVGBVv9ICQLUYL4fQHlQ) - Flan T5 is a great go to model for text classification. 

Tiny titans - Can smaller LLM models punch above their weight for meeting summarization

Originally posted by @manisnesan in #47 (comment)

Questions

• What are the datasets available
• What are the key constituents involved in an effective meeting summary
• What are the challenges involved in creating effective meeting summarization
• What are the most recent advancements in meeting summarization tech
• How is this different from other diverse summarization involved in news, science, technology, medical
• How meeting summarization is related customer service call summarization?
  • meeting (multi party interactions - more than two speakers) where as customer service call or medical appoints l is a biparty interaction ie only two speakers are involved.

[manisnesan]

Meeting Summarization

Meeting summarization is the process of creating a concise overview of the key points, decisions, and action items discussed during a meeting[1]. It serves to keep stakeholders informed, facilitate decision-making, encourage accountability, and enhance communication[1].

There are several proven ways to summarize a meeting effectively:

1. Take concise notes during the meeting, focusing on the most important information[1].

2. Use a clear and organized format in the summary, such as including the date, time, location, attendees, agenda items, discussion points, decisions, action items, and next steps[1].

3. Follow and fill out the meeting agenda when creating the summary notes[1].

4. Summarize the meeting over email to all participants after the fact[1].

5. Use AI tools to automatically generate meeting summaries from transcripts[1][2].

Challenges in meeting summarization include the difficulty of collecting confidential meeting data, the labor-intensive process of annotating summaries, and the need to capture key issues while excluding irrelevant discussions[4][5]. Recent research has focused on creating benchmark datasets[3][4][5] and developing advanced summarization models[2][3].

In summary, meeting summarization is a crucial skill for keeping teams aligned and productive, with various manual and automated techniques available to create high-quality summaries efficiently.

Citations:
[1] https://fireflies.ai/blog/summarize-a-meeting
[2] https://github.com/topics/meeting-summarization
[3] https://paperswithcode.com/task/meeting-summarization
[4] https://arxiv.org/abs/2305.17529
[5] https://aclanthology.org/2023.acl-long.906.pdf

[manisnesan]

Diverse Summarization Dataset

From Pegasus - Paper

[manisnesan]

From Abstractive Meeting Summarization

• A system that could reliably identify and sum up the most important points of a conversation would be valuable in a wide variety of real-world contexts, from business meetings to medical consultations to customer service calls.

Customer Service Calls could be multi-party conversation but only two party speak in a given time span. Also the format of the meeting in customer service is problem solving in nature.

Eg: Customer Rep - Agent 1 ---> Customer Rep - Agent 2 ----> Customer Rep -- Agent 3

Related: Abstractive Dialogue summarization, Abstractive Text Summarization, Meeting Summariziation, text Generation

Stages in abstractive

• Selection of important points that are worthy enough. This is same as extractive summarization.

• Synthesis

• language generation
  

• Figure 1 shows excerpts of the human-made extractive (left column) and abstractive (right col- umn) summaries of meeting ES2011c.

• The col- ored lines relate each abstractive sentence to the set of extractive sentences—the abstractive com- munity—that annotators judged as supporting it.

[manisnesan]

Differences from traditional summarization

• linguistic interactions involved in the meetings
• multiparty conversations

[manisnesan]

From Call Summarization: why it is important and what it is possible today and in a near future

• It aims to automatically generate concise, fluent summaries capturing the key points of a conversation, which can help improve customer experience and reduce agent workload

"AUTOMATIC SUMMARIZATION OF CALL-CENTER CONVERSATION" by E. Stepanov, B. Favre, F. Alam, S. Chowdhury, K. Singla, J. Trione, F. Be ́chet, G. Riccardi. offers a hybrid approach using both extractive/abstractive.

See

• "State of the Art Summarisation Techniques", Information Systems Seminar 19/20 by Anna Franziska Bothe, Alex Truesdale, LukasKolbe
• https://humboldtwi.github.io/blog/research/information_systems_1920/nlp_text_summarization_techniques/

[manisnesan]

From Generating Abstractive Summaries from Meeting Transcripts

[manisnesan]

Challenges involved

Nature of meeting-style speech :

• leads to low information density & high noise
• significantly longer eg: AMI transcript tokens 4, 757 & its summary 322
• constrasting to two speaker conversations - multiparty conversations has challenges to speaker & addressee identification

Preference for abstractive summarization

• LEAD-3 baseline - extractive methods - first 3 sentences of a doc
• selection of important material

Heterogeneous meeting formats

• sharing info or brainstorming ideas
• depending on meeting formats require a variety of automatic systems

Subjectivity

• reformulating the same content in different words & style
• what is counted as summary-worth

[manisnesan]

See the example case study from Orca paper on Meeting Transcript processing

Example from the paper

System

You are a teacher. Given a task, you explain in simple steps what the task is asking, any guidelines it provides, and how to use those guidelines it provides to find the answer.

User

You will read a meeting transcript, then extract the relevant segments to answer the following question

Question: How does Steven feel about selling?

$Meeting_Transcript

Please answer the following question
Question: How does Steven feel about selling?

Extract from transcript the most relevant segments for the answer, then answer the question.

[manisnesan]

https://www.reddit.com/r/LocalLLaMA/s/xeSFTXwa5q

[manisnesan]

https://community.openai.com/t/how-to-summarize-large-research-articles/142730

[manisnesan]

Five levels of summarizing Youtube

• langchain map reduce is an interesting idea showcased
• Topic modelling using language models is also another interesting approach here

Usecase

YouTube Videos - Auto Chapter Generation
Podcasts - Extract structured information
Meeting Notes - Send topic summaries to participants
Town Hall Meetings - Structured information
Earnings Report Calls - Sell structured data to investment groups
Legal Documents - Quickly summarize by topic
Movie Scripts - Quick bullet points for production recaps
Books - Auto generate table of contents

[manisnesan]

PYDATA - NYC 2024 The Art of Compression: Crafting Insightful Summaries with LLMs

As Large Language Models continue to advance, their application in text summarization presents both powerful opportunities and specific challenges. This talk will focus on practical strategies to overcome the limitations posed by context windows—a critical factor when dealing with extensive texts. The talk will also demonstrate how fine-tuning can improve summarization tasks for domain specific private datasets and when to use what. Attendees will learn how to build an end-to-end summarization workflow, with a focus on effective data chunking, prompt optimization, and advanced evaluation methods to ensure accurate and meaningful summaries.
The session will cover three key summarization techniques—stuff, refine, and map-reduce—explaining when and how to use each approach. In addition, we’ll explore the latest in evaluation metrics, demonstrating how to leverage more sophisticated models as judges to refine and assess the quality of summaries.

Outline:

• Introduction to the summarization problem
• Understanding LLM context windows
• Chunking techniques and their applications
• Overview of summarization techniques (stuff, refine, map-reduce)
• Fine-tuning on a custom dataset
• Introduction to Langchain and its role in summarization
• Evaluation metrics
• Using LLMs as judges
• LLM production deployment

Background Knowledge Required:

• Basic understanding of LLMs
• Familiarity with Python

Presentation - https://github.com/aartij22/Pydata-NYC-2024