텍스트 요약 분야의 주요 연구 주제, Must-read Papers, 이용 가능한 model 및 data 등을 추천 자료와 함께 정리한 저장소입니다.

Revolutionize your mind mapping with this repo, combining mermaid and GPT-3.5-turbo / GPT-4! Craft captivating mindmaps for brainstorming and text summarization, unlocking creativity and productivity. Experience this trailblazing solution for the ultimate mind mapping experience today! Don't miss out and elevate your creative thinking skills!

The official repo of our research work "Interactive Editing for Text Summarization".

Repo for Enhanced Seq2Seq Autoencoder via Contrastive Learning for Abstractive Text Summarization

This is the official repo of Text Summarizer Streamlit App video from AI Anytime YouTube channel.

Repo for Topic-Guided Abstractive Text Summarization

Repo for Topic-Aware Abstractive Text Summarization

Interview Coding Challenge Data Science Step 1 of the Data Scientist Interview process. Follow the instructions below to complete this portion of the interview. Please note, although we do not set a time limit for this challenge, we recommend completing it as soon as possible as we evaluate candidates on a first come, first serve basis... If you have any questions, please feel free to email support@TheZig.io. We will do our best to clarify any issues you come across. Prerequisites: A Text Editor - We recommend Visual Studio Code for the ClientSide code, its lightweight, powerful and Free! (https://code.visualstudio.com/) SQL Server Management Studio (https://docs.microsoft.com/en-us/sql/ssms/download-sql-server-management-studio-ssms?view=sql-server-2017) R - Software Environment for statitistal computing and graphics. You can download R at the mirrors listed here (https://cran.r-project.org/mirrors.html) Azure - Microsoft's Cloud Computing platform. You can create an account without a credit card by using the Azure Pass available at this link (https://azure.microsoft.com/en-us/offers/azure-pass/) Git - For source control and committing your final solution to a new private repo (https://git-scm.com/downloads) a. If you're not very familiar with git commands, here's a helpful cheatsheet (https://services.github.com/on-demand/downloads/github-git-cheat-sheet.pdf) 'R' Challenge For each numbered section below, write R code and comments to solve the problem or to show your rationale. For sections that ask you to give outputs, provide outputs in separate files and name them with the section number and the word output "Section 1 - Output". Create a private repo and submit your modified R script along with any supporting files. Load in the dataset from the accompanying file "account-defaults.csv" This dataset contains information about loan accounts that either went delinquent or stayed current on payments within the loan's first year. FirstYearDelinquency is the outcome variable, all others are predictors. The objective of modeling with this dataset is to be able to predict the probability that new accounts will become delinquent; it is primarily valuable to understand lower-risk accounts versus higher-risk accounts (and not just to predict 'yes' or 'no' for new accounts). FirstYearDelinquency - indicates whether the loan went delinquent within the first year of the loan's life (values of 1) AgeOldestIdentityRecord - number of months since the first record was reported by a national credit source AgeOldestAccount - number of months since the oldest account was opened AgeNewestAutoAccount - number of months since the most recent auto loan or lease account was opened TotalInquiries - total number of credit inquiries on record AvgAgeAutoAccounts - average number of months since auto loan or lease accounts were opened TotalAutoAccountsNeverDelinquent - total number of auto loan or lease accounts that were never delinquent WorstDelinquency - worst status of days-delinquent on an account in the first 12 months of an account's life; values of '400' indicate '400 or greater' HasInquiryTelecomm - indicates whether one or more telecommunications credit inquires are on record within the last 12 months (values of 1) Perform an exploratory data analysis on the accounts data In your analysis include summary statistics and visualizations of the distributions and relationships. Build one or more predictive model(s) on the accounts data using regression techniques Identify the strongest predictor variables and provide interpretations. Identify and explain issues with the model(s) such as collinearity, etc. Calculate predictions and show model performance on out-of-sample data. Summarize out-of-sample data in tiers from highest-risk to lowest-risk. Split up the dataset by the WorstDelinquency variable. For each subset, run a simple regression of FirstYearDelinquency ~ TotalInquiries. Extract the predictor's coefficient and p-value from each model. Store the in a list where the names of the list correspond to the values of WorstDelinquency. Load in the dataset from the accompanying file "vehicle-depreciation.csv". The dataset contains information about vehicles that our company purchases at auction, sells to customers, repossess from defaulted accounts, and finally re-sell at auction to recover some of our losses. Perform an analysis and/or build a predictive model that provides a method to estimate the depreciation of vehicle worth (from auction purchase to auction sale). Use whatever techniques you want to provide insight into the dataset and walk us through your results - this is your chance to show off your analytical and storytelling skills! CustomerGrade - the credit risk grade of the customer AuctionPurchaseDate - the date that the vehicle was purchased at auction AuctionPurchaseAmount - the dollar amount spent purchasing the vehicle at auction AuctionSaleDate - the date that the vehicle was sold at auction AuctionSaleAmount - the dollar amount received for selling the vehicle at auction VehicleType - the high-level class of the vehicle Year - the year of the vehicle Make - the make of the vehicle Model - the model of the vehicle Trim - the trim of the vehicle BodyType - the body style of the vehicle AuctionPurchaseOdometer - the odometer value of the vehicle at the time of purchase at the auction AutomaticTransmission - indicates (with value of 1) whether the vehicle has an automatic transmission DriveType - the drivetrain type of the vehicle

This repo serves as implementation of several seq2seq model, most focused on text summarization

This repo contains text summarization use cases

A repo with abstractive text summarization retraining BART model with MLM task

This repo contains code for text summarization using extractive method and flask is for working in browser

Repo for my master thesis. Program to structure text, images and tables in JSON with Data/MetaData/Summarization layers for structured server-side querying.

An interactive terminal TUI for exploring and summarizing GitHub commits. Built in Go with Bubble Tea and GitHub CLI, featuring multi-repo support, filtering, statistics, and export to text, Markdown, or JSON.

An experimental repo for testing effective text summarization tools.

A readme.md repo for AI project in Text Summarization

This repo contains a notebook that performs text summarization using LSTM and attention mechanism.

This repo essentially contains the code for the first test of text summarization software

This repo is all about an implementation of Text summarization using hugging face api

"Text Summarization Using NLP" is a Python repo employing spaCy for concise text summaries. Explore extractive techniques, follow a simple setup, and dive into effective NLP-based summarization.

In this repo I have implemented text summarization from websites url using NLTK using sentence scores.

This repo contains deep learning models for Text Summarization and related experiments done by my team.

This repo contains codes for summarization of NOAA api JSON data into human readable texts for forecasting

This repo aims to store the code of a text summarizer app

Replit repo link https://replit.com/@prathyushagundl/AI-Text-Summarizer-App-Starter-Template

Repo containing NLP projects ranging from text summarization, sentiment analysis and classification analysis using different sequence based models in TensorFlow and utilizing AWS for cloud computing.

a demo repo for building out a live text processing tool - should allow user to extract text and summarize logs from a video stream

This repo takes in a text file and then returns a user selected number of sentences that best summarize the document.

Welcome to texTrim repo. It hosts a cutting-edge web application designed to simplify and enhance the process of summarizing text using AI.

This repo contains the NLP Project i completed at IIT Chicago. It is an extractive based Text summarization technique that is similar to LexRank and TextRank , but also uses word embeddings to measure similarity between sentences