lwhieldon/abstracttextsummarization

Hugging Face Transformers to build abstract text summarization NLP Model

Abstract Text Summarization Using Transformers

Hugging Face Transformers to build abstract text summarization NLP Model

Overview and Background

Text summarization is a complex task for recurrent neural networks, particularly in neural language models. Despite it's complexity, text summarization offers the prospect for domain experts to significantly increase productivity and is used in enterprise-level capacities today to condense common domain knowledge, summarize complex corpus of text like contracts, and automatically generate content for use cases in social media, advertising, and more. In this project, I explore the use of large language models in the recurrent neural network framework using encoder-decoder transformers from scratch to condense dialogues between several people into a crisp summary, demonstrating abstract text summarization. Applications of this exercise are endless, but could be especially beneficial for summarizing long transcripts from meetings and so on.

Let's first look at the dataset we will use for training: Samsung transcript data. We will then go into the scoring parameters and demonstrate how we train the model. Lastly, we will then showcase our model's inference and discuss opportunities for future work and study use cases.

Data & Model Details

For our application, we'll use the SAMsum dataset, developed by Samsung, which consists of a collection of dialogues along with brief summaries. In an enterprise setting, these dialogues might represent the interactions between a customer and a support center personnel or a transcript representing individuals taking part in a meeting, so generating accurate summaries can help improve customer service, cut down on note taking, and detect common patterns among customer requests or meeting themes.

For this project, we leverage 🤗 Hugging Face's SAMsum dataset by leveraging the load_dataset library. This is beneficial as Hugging Face has already performed the task of cleansing and organizing the SAMsum dataset for us.

The dataset has 3 features:

• Dialogue, which contains the dialogue text,
• Summary containing the synopsis of the dialogue, and
• id to uniquely identify each record.

🤗 Hugging Face's dataset is made of 16,369 conversations distributed uniformly into 4 groups based on the number of utterances in conversations: 3-6, 7-12, 13-18, and 19-30. Each utterance contains the names of the speaker. Note also the data is split into the following subsets:

Data Splits

• train: 14,732 records
• validation: 818 records
• test: 819 records

Pegasus-Samsum Model

This model is a fine-tuned version of google/pegasus-cnn_dailymail on the samsum dataset (). It achieves the following results on the evaluation set:

• Loss: 1.4919

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 16
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
1.6776	0.54	500	1.4919

YouTube Presentation

To support the submission of this project to UMBC's Data Science Program, class DATA606: Capstone in Data Science, here is the youtube containing presentation.

Table of Contents

This assignment contains the following areas:

1. Summary and Report: Jupyter Notebook including a detailed abstract on problems in assignment, code relevant to project, and visualizations supporting the completion of the project.
2. Dataset: Zipped copy of the dataset should the reader like to export for their analysis purposes.
3. Presentation: Presentation Conducted for this project

References

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• A. See, P. J. Liu, and C. D. Manning, “Get To The Point: Summarization with Pointer-Generator Networks,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vancouver, Canada, Jul. 2017, pp. 1073–1083. doi: 10.18653/v1/P17-1099.
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Project Curation

Note also that notebooks were created in Google Collaboratory to take advantage of GPU throughput to speed the training of the Transformers.

Contributors : Lee Whieldon

Languages    : Python
Tools/IDE    : Google Colab, Visual Studio Code
Libraries    : Transformers 4.22.2, Pytorch 1.12.1+gpu, Datasets 2.4.0, Tokenizers 0.12.1

Assignment Submitted     : December 2022