CS Senior Comps

A lot of thoughts and coding happened this week. So let's get into it... two_models This is a branch that incorporates an off-the-shelf  Coreference Resolution  model during pre-processing and post-processing of the data. I'll go through what this branch of code does an then explain why I decided to make these decisions. The  Coreference Resolution  model that I'm using is from this repo  which utilizes spaCy and Neural Networks to identify the pronouns and which noun the pronoun is referring to. Using this model, I run all the data that is used by the Pointer-Generator model to train; specifically, I run the reference summary (the summary that is written by a human of the given document). By running the training data through the Coreference Resolution model, I can utilize spaCy to identify clusters  within the document (individual data). These clusters contain pretty much a dictionary of the noun and the pronouns/nouns that refer to the noun ( e.g...

From the feedback I've received about my previous blogs, I can see how confusing the posts might have been and how unrelated they are to my project. So let me take a few moments to address these issues. Regarding the topics discussed from the blog post titled Updated 9/23 , I talked about some of the common problems many automatic text summarization tools face. There have been many research and solutions to the problems that were raised and I decided to use Google/Stanford's model which utilizes some of the solutions discussed in the blog post. These problems are considered to be generally solved by many of today's models including that of Google.  Blog Post #3  outlines the general features implemented in the model I chose to work with. I also talk about some of the problems I faced in running the model on my own system to make sure it works and that I can use it further for my comps project. I also briefly mentioned a problem that the model still faces: reference reso...