## Inspiration

We were inspired to increase the public's access to information stored in lengthy videos. From personal experiences, we found it difficult to find what we were looking for in long video lectures, news reports, or earnings calls. This inspired us to built snipped, a better, faster, and more efficient way to access the information we were searching for.

## What it does

The product is a web application that summarizes long videos based on the videos' captions. Snipped finds the key subjects and topics discussed in a video, and also pinpoints their location within the video, allowing viewers to jump to the topics they are interested in. It also visualizes data about the video's keywords and displays graphs providing a sentiment based analysis of the video.

## How I built it

Our strategy was to use a video API to extract the captions from YouTube videos and then to use IBM's Alchemy API to develop a subject ranking algorithm for the topics in the video. We also used the Wikipedia API to display a short summary of each key word. We also linked the user to the specific time in the YouTube video where the certain key word was discussed.

## Challenges I ran into

Our biggest challenges was trying to learn how to implement a variety of APIs into a cohesive, well-organized, summarization algorithm.

## Accomplishments that I'm proud of

We're proud of the educational application of our product. If a student doesn't have time to watch long video lectures, the student could use our product to study only the concepts they find most difficult. Our product also gives users the opportunity to learn more about how important certain concepts are to a lecture and to research more information about those concepts.

## What I learned

We learned how to use and integrate the various APIs we relied upon. We also got better at quickly finding bugs and learned skills applicable to general about web development.

## What's next for Snipped

We hope we can continue to develop and refine Snipped so it becomes an extremely useful resource for individuals to acquire information from lengthy videos.

(Our product currently only works with youtube videos with closed captions)
## Inspiration

As YouTube consumers, we find it extremely frustrating to deal with unnecessarily long product reviews that deliver numerous midroll ads rather than providing useful and convenient information. Inspired, we wanted to create an application that stops all that by allowing users to glean the important information from a video without wasting time.

## What It Does

* Determines whether a YouTube product review is positive or negative.
* Summarizes the contents of the review.

## How We Built It

* The auto-generated closed captions for a YouTube video are obtained
* Punctuation is added to YouTube auto-generated closed captions.
* The punctuated script retrieved is then fed into Cohere, an NLP toolkit.
* Cohere performs an analysis of the script and returns sentiment and summary.
* Chrome extension is then utilized to display the information to the user.

## Challenges We Ran Into

* Settling on a use of Cohere.
* Punctuating the auto-generated YouTube closed captions.

## Accomplishments That We're Proud Of

* Deftly transitioning between ideas.
* Combining many new technologies.
* Figuring out the puntuator2 library.

## What We Learned

* How to develop chrome extensions.
* How to utilize Cohere to perform analysis and process summaries.

## What's Next For The Project

* Implement a mobile app component that allows the user's to make use of the service at the convenience of their phone.
* Keep track of users' past history and engagement.
* Incorporate a points system that encourages and drives individuals to continue to make use of the application.
## Inspiration

Due to COVID-19, millions of students across the world have been forced to quickly adapt to video lectures and online education. To ease this transition and make studying more efficient, we wanted to help students by summarizing their lecture transcripts while also capturing the full lecture transcript so students can pay full attention to the lecture instead of being distracted with taking notes.

## What it does

A website that summarizes video lectures for efficient studying. Users can upload their video lectures to receive a full text transcript and timestamps of the most important sentences. The video timestamps have not been formatted to "minutes : seconds" yet.

## How We built it

We made made a microservice out of a pre-trained BERT model to summarize text, and an Express web server that works with Vue for the UI to make a web app. The web app accepts video uploads, sends the video to Azure's Speech-to-Text API to get a full transcript, sends the transcript to the microservice to get a summary with timestamps, and sends the summary and timestamps to Vue for display and video playback.

## Challenges we ran into

Managing cloud platform credentials in a team of 4 was difficult. Coordinating ourselves to avoid duplicating work. Managing packages and dependencies. Scope creep. Timestamps need to formatted to "minutes : seconds".

## Accomplishments that we're proud of

Most technically sophisticated hackathon project so far, as the project has many moving parts like Azure Media Services (for video playback), Azure Cognitive Services (for the Speech-to-Text API), and BERT (for text summarization).
Fun name ;)

## What we learned

CORS, JavaScript Promises, Microservice Architecture

## What's next for AweSummarizer

Adding video lecture subtitles automatically generated with Azure Media Services. Adding clickable timestamps to play the video at that timestamp for more convenient navigation.