This project aims to develop a system capable of automatically summarizing short dialogue text. This addresses the challenge of extracting concise yet informative summaries from conversational exchanges, enabling users to quickly grasp the information of the dialogues.

Summarizing these conversations can be valuable for various applications, such as:

• Streamlining information retrieval in customer service interactions
• Condensing meeting discussions for efficient review
• Providing concise overviews of chat conversations on social media platforms

This project tackles the task of automatically generating concise summaries, saving users time and effort while improving comprehension.

Source: Google Research

Input: Dialogue text

Example:

Matt: Do you want to go for date? 
Agnes: Wow! You caught me out with this question Matt. 
...
Agnes: See you on saturday. 
Matt: Yes, looking forward to it. 
Agnes: Me too.

Output: Summarized dialogue

Example:

Matt invites Agnes for a date to get to know each other better. They'll go to the Georgian restaurant in Kazimierz on Saturday at 6 pm, and he'll pick her up on the way to the place.

We'll utilize the DialogSum dataset accessible from 🤗Hugging Face (https://huggingface.co/datasets/knkarthick/dialogsum) and Paper (https://arxiv.org/pdf/2105.06762.pdf). This dataset comprises real-life dialogue scenarios paired with corresponding manually crafted summaries and dialogue topics.

DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 (Plus 100 holdout data for topic generation) dialogues with corresponding manually labeled summaries and topics.

Here's a sample of the DialogSum dataset structure:

This project explores two powerful LLMs well-suited for dialogue summarization:

• FLAN-T5: This model excels at understanding complex relationships within text, making it effective in summarizing the nuances of conversations.
• BART: This model boasts strong capabilities in text generation tasks, making it adept at generating informative and well-structured summaries.

To tailor these LLMs specifically for dialogue summarization, we will investigate several fine-tuning approaches:

• Instruction Fine-tuning
• Parameter Efficient Fine Tuning (PEFT)
  • Low-Rank Adaptation (LoRA)
  • Quantized Low-Rank Adaptation (QLoRA)

!git clone "https://github.com/dtruong46me/dialogue-text-summarization.git"

Supervisor: Prof. Le Thanh Huong

Student Group:

!pip install -q streamlit

!wget -q -O - ipv4.icanhazip.com

!npm install -g localtunnel -q

!streamlit run "/kaggle/working/dialogue-text-summarization/streamlit_app.py" & npx localtunnel --port 8501