MeetSum: Transforming Meeting Transcript Summarization using Transformers!
Nima Sadri, Bohan Zhang, Bihan Liu

TL;DR
This paper introduces MeetSum, a Transformer-based model with a Pointer Generator and coverage mechanism, that effectively summarizes meeting transcripts, outperforming previous models through zero-shot and fine-tuning strategies.
Contribution
The paper presents a novel Transformer-based summarization model for meetings that leverages zero-shot learning and out-of-domain pretraining to improve performance.
Findings
Achieved at least 5 ROUGE-2 score improvement over previous models.
Zero-shot training on news data outperforms training on meeting data.
Fine-tuning on meeting data further enhances summarization quality.
Abstract
Creating abstractive summaries from meeting transcripts has proven to be challenging due to the limited amount of labeled data available for training neural network models. Moreover, Transformer-based architectures have proven to beat state-of-the-art models in summarizing news data. In this paper, we utilize a Transformer-based Pointer Generator Network to generate abstract summaries for meeting transcripts. This model uses 2 LSTMs as an encoder and a decoder, a Pointer network which copies words from the inputted text, and a Generator network to produce out-of-vocabulary words (hence making the summary abstractive). Moreover, a coverage mechanism is used to avoid repetition of words in the generated summary. First, we show that training the model on a news summary dataset and using zero-shot learning to test it on the meeting dataset proves to produce better results than training it…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Genomics and Phylogenetic Studies
Methods[LivE@PeRson]How do I talk to a real person at Expedia? · Tanh Activation · Sigmoid Activation · Long Short-Term Memory · Softmax · Pointer Network
