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Teachers often conduct surveys in order to collect data from a predefined group of students to gain insights into topics of interest. When analyzing surveys with open-ended textual responses, it is extremely time-consuming, labor-intensive, and difficult to manually process all the responses into an insightful and comprehensive report. In the analysis step, traditionally, the teacher has to read each of the responses and decide on how to group them in order to extract insightful information. Even though it is possible to group the responses only using certain keywords, such an approach would be limited since it not only fails to account for embedded contexts but also cannot detect polysemous words or phrases and semantics that are not expressible in single words. In this work, we present a novel end-to-end context-aware framework that extracts, aggregates, and abbreviates embedded semantic patterns in open-response survey data. Our framework relies on a pre-trained natural language model in order to encode the textual data into semantic vectors. The encoded vectors then get clustered either into an optimally tuned number of groups or into a set of groups with pre-specified titles. In the former case, the clusters are then further analyzed to extract a representative set of keywords or summary sentences that serve as the labels of the clusters. In our framework, for the designated clusters, we finally provide context-aware wordclouds that demonstrate the semantically prominent keywords within each group. Honoring user privacy, we have successfully built the on-device implementation of our framework suitable for real-time analysis on mobile devices and have tested it on a synthetic dataset. Our framework reduces the costs at-scale by automating the process of extracting the most insightful information pieces from survey data.

In this work, we study abstractive text summarization by exploring different models such as LSTM-encoder-decoder with attention, pointer-generator networks, coverage mechanisms, and transformers. Upon extensive and careful hyperparameter tuning we compare the proposed architectures against each other for the abstractive text summarization task. Finally, as an extension of our work, we apply our text summarization model as a feature extractor for a fake news detection task where the news articles prior to classification will be summarized and the results are compared against the classification using only the original news text. keywords: LSTM, encoder-deconder, abstractive text summarization, pointer-generator, coverage mechanism, transformers, fake news detection