Summarization and keyword extraction on customer feedback data : Comparing different unsupervised methods for extracting trends and insight from text

Abstract: Polestar has during the last couple of months more than doubled its amount of customer feedback, and the forecast for the future is that this amount will increase even more. Manually reading this feedback is expensive and time-consuming, and for this reason there's a need to automatically analyse the customer feedback. The company wants to understand the customer and extract trends and topics that concerns the consumer in order to improve the customer experience. Over the last couple of years as Natural Language Processing developed immensely, new state of the art language models have pushed the boundaries in all type of benchmark tasks. In this thesis have three different extractive summarization models and three different keyword extraction methods been tested and evaluated based on two different quantitative measures and human evaluation to extract information from text. This master thesis has shown that extractive summarization models with a Transformer-based text representation are best at capturing the context in a text. Based on the quantitative results and the company's needs, Textrank with a Transformer-based embedding was chosen as the final extractive summarization model. For Keywords extraction was the best overall model YAKE!, based on the quantitative measure and human validation