Automatic Text Summarization of Customer Reviews

In recent years there were a lot of advances in natural language processing. Some of them, especially base techniques of processing text are mostly solved like part-of-speech tagging or named entity recognition. On the other hand, there is also many tasks, that has a lot work to do like creating open dialog systems, paraphrase generation or text summarization. In last few years a new problems, connected to era of internet, were introduced like mining social media. With exponential growth of data and texts on internet tasks like text summarization become significantly more important.
Task of text summarization is know for a very long period. In late 50s Luhn tried to automatically create abstract of documents. Over decades there have been many summarization systems dealing both with extractive and abstractive summarization. In recent years there have been significant progress in abstractive summarization of news articles.
Interesting part of text summarization is summarization of opinion in comments or customer reviews. With increasing number of comments on social networks and reviews on services and product is still more difficult to get right opinion or choose product wisely. Summarization of customer reviews can help either customers to decide, which product buy, but also can help producers and owners of services to improve their services or product in future.