RiemannAI is a SaaS designed for text summarization through by using Machine Learning. We provide both users, developers, and students with all of their text summarization tools needed to succeed.
RiemannAI uses Microsoft Azure’s speech recognition, translation, and summarization models to pick up speech from the user’s device and output a result. Users can record audio in a variety of languages from Chinese to Portuguese, with RiemannAI translating, summarizing, and giving it back to the user in their desired langauge.
Extractive & Abstractive
RiemannAI provides both extractive and abstractive simplification techniques to reduce both the complexity and length of text. Our extractive summarization model features a custom built machine learning model and abstractive summarization extends upon Google's Allen NLP model.
Saving to Riemann DB
With RiemannAI, we propose a methodoloy of storing both the translated and summarized text into local storage systems. Users are able to view past summaries and access them on-demand. In this demo, sample data is provided for demonstration purposes.
As the world becomes more and more connected, the demand for foreign academic resources rises. English language learners (or ELLs) are defined as students who are unable to communicate or learn effectively in English, many of whom are English as a second language (or ESL) students. In the United States alone, 10.4%, or 1 in 10, of all students fall under this category (nces.ed.gov). However, many of these students’ schools lack the resources to help them bridge the language barrier. As ESL students ourselves, we have experienced such challenges in our own lives.
RiemannAI is a simplistic language process interface. The key feature of our solution allows students to use RiemannAI to transcribe and translate audio recordings of lectures, discussions, or speeches from most languages into the student’s chosen language. To make the review of the material easier for these students, our solution also incorporates a text summarization capability that returns the main points from this audio recording for the student’s use. Yet RiemannAI is more than merely just a single tool. The interactive interface also introduces methods for extractive and abstractive summarization from the text (expanding its functionality to more than just audio input). The extractive summarization functionality allows for students to shorten long, difficult texts to an approachable length. On the other hand, the abstractive summarization function aids in the summarization and finding of answers to certain questions students may have. This, in turn, will allow students to approach work completion and studying more efficiently.
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