Conversation Summarizer Bot

About Client

Industry

Customer Service

Location

United Kingdom

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About Client

Appraisal Management Company
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Industry
Real Estate​
Location
USA
Project Overview
Traditional Process
Challenges
Our Solution
Use-Cases of Conversation Summarization Bot
Features
Technologies Used

Project Overview

We identified a need for a chatbot with advanced summarization capabilities to address the challenges associated with managing and extracting valuable insights from large volumes of textual data. 

This innovative solution aims to provide users with a quick and efficient way to distill key information from lengthy interactions, such as live meetings, conversations, support tickets, and other text forms, into concise summaries. 

The chatbot’s core functionality is to summarize conversations in real-time using machine learning models. The chatbot is designed to be versatile and can be implemented in various areas, including note-taking, instruction creation, review and feedback systems, and more.

Traditional Process of Conversation Summarization

01
Manual Summarization: In traditional settings, summarizing conversations or textual data involves manually reading through the content and distilling the key points into a concise summary. This process is time-consuming and prone to errors, especially when dealing with large volumes of data.
02
Lack of Real-time Summarization: Traditional methods do not offer real-time summarization capabilities, meaning that users have to wait until the conversation or textual data is complete before a summary can be generated.
03
Limited Scalability: Manual summarization is not scalable for large volumes of data or real-time applications, making it unsuitable for use cases such as live meetings or continuous conversations.

Challenges in Traditional Process

01
Information Overload: With the increasing volume of textual data generated in various forms such as meetings, conversations, and support tickets, users often struggle to extract relevant information efficiently.
02
Time Constraints: Users face time constraints when dealing with large volumes of textual data, making it challenging to manually summarize and extract key insights in a timely manner.
03
Lack of Automation: Existing methods lack automation and real-time capabilities, leading to inefficiencies and delays in information processing.

Our Solution

The business idea revolves around developing a chatbot with advanced machine learning models for real-time conversation summarization. The key features include:

01
Real-time Summarization: The chatbot provides real-time summarization of conversations, allowing users to view summaries as the conversation progresses.
02
Machine Learning Model: The chatbot leverages machine learning models to analyze and summarize conversations, ensuring accuracy and efficiency in extracting key information.
03
Versatility: The chatbot can be implemented in various areas, including note creation, instruction generation, review and feedback systems, and more, making it a versatile tool for different use cases.
04
Integration Capabilities: The chatbot can be integrated with various platforms and tools to support seamless integration into existing workflows and systems.
05
Scalability: The chatbot is designed to handle large volumes of data and can be scaled to support multiple users and applications simultaneously.

Use-Cases of Conversation Summarization Bot

01
Live Meeting Summarization: The chatbot can summarize live meetings, providing participants with a quick overview of the discussion’s key points.
02
Continuous Conversation Summarization: If a third person joins a conversation, the chatbot can provide a summary of the previous conversation to bring the new participant up to speed.
03
Notes and Instructions: The chatbot can be used to create notes and instructions based on conversations, making it easier to capture and document important information.
04
Support Ticket Summarization: The chatbot can distill critical information from support tickets, enabling support teams to quickly identify and address customer issues.
05
Video, Audio, and Text Summarization: The chatbot can convert long videos, audio recordings, and textual data into summarized text, making it easier to consume and analyze.

Time and Cost Savings: Automated processes save time and reduce resource requirements.

Improved Data Accuracy: Automation reduces the risk of errors in survey content and data entry.

Enhanced Decision-Making: Real-time analytics provide actionable insights for better decision-making.

Increased User Engagement: Convenient survey submission within familiar chat platforms increases user participation

Benefits in Numbers: As below

100% boost in ease of conducting surveys

95% improved quality and time of decision making

90% reduction in delays

80% improved client feedback

Features of Conversation Summarization Bot

01
Multi-format Summarization: Supports summarization of text, audio, and video content.
02
Real-time Summarization: Provides instant summaries of ongoing conversations or textual data.
03
Scalability: Capable of handling large volumes of data and multiple users simultaneously.
04
Machine Learning Models: Utilizes advanced machine learning algorithms to ensure accurate and efficient summarization.
05
Versatility: Can be applied to various use cases such as note creation, instruction generation, and review systems.
06
Integration Capabilities: Easily integrates with existing platforms and tools for seamless deployment.

Administrators can authenticate and log in to the app using Single Sign-On functionality through Slack or Microsoft Teams platforms.

Users have the ability to create and modify surveys within the app.

Users can configure the channels or teams within Slack or Microsoft Teams where they want to grant access to the survey.

Users have the flexibility to configure the frequency at which the survey is offered.

The app automatically sends out surveys based on the configured frequency set by the user.

The app enables users to receive and collect survey responses efficiently.

The app can send real-time updates of survey submissions to the administrator's Slack or Microsoft Teams platform.

Technologies Used

01

We have used the SAMSum (A Human-annotated Dialogue Dataset for Abstractive Summarization) dataset, which contains more than 16k messenger-like conversations with summaries, written down by linguists fluent in English.

02

We have fine-tuned the SAMsum Corpus by a highly accurate SOTA (state-of-the-art) pre-trained Facebook BART (large-sized model) which is a seq2seq model, it takes a sequence of words as input and outputs a summary of the input. 

03

The evaluation metric used is ROUGE. The ROUGE-1 metric score for the fine-tuned BART (large-sized model) on the SAMSum dataset is 42.10 on the unseen (Test) set. Which outperforms previous work on summarization.