  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 on the AMI meeting
dataset. Second, we show that training this model first on out-of-domain data,
such as the CNN-Dailymail dataset, followed by a fine-tuning stage on the AMI
meeting dataset is able to improve the performance of the model significantly.
We test our model on a testing set from the AMI dataset and report the ROUGE-2
score of the generated summary to compare with previous literature. We also
report the Factual score of our summaries since it is a better benchmark for
abstractive summaries since the ROUGE-2 score is limited to measuring
word-overlaps. We show that our improved model is able to improve on previous
models by at least 5 ROUGE-2 scores, which is a substantial improvement. Also,
a qualitative analysis of the summaries generated by our model shows that these
summaries and human-readable and indeed capture most of the important
information from the transcripts.
