110 Open source projects that are alternatives of or similar to summarize-webpage

Natural Language Processing notes and implementations.

Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer.

A module for E-mail Summarization which uses clustering of skip-thought sentence embeddings.

An implementation of transformer-based language model for sentence rewriting tasks such as summarization, simplification, and grammatical error correction.

keras project that parses and analyze english resumes

Automatic Text Summarization (English/Arabic).

📹 A tool for automatic video creation and uploading on YouTube

Natural Language Processing using NLTK and Spacy

This repository contains the code, data, and models of the paper titled "XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages" published in Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021.

Creating a software for automatic monitoring in online proctoring

An affect generator based on TextBlob and the NRC affect lexicon. Note that lexicon license is for research purposes only.

Ruby port of the NLTK Punkt sentence segmentation algorithm

A company/project name generator for Python. Uses NLTK and diverse techniques derived from existing corporate etymologies and naming agencies for sophisticated word generation and ideation.

A tool to automatically summarize documents abstractively using the BART or PreSumm Machine Learning Model.

A simple python implementation of the Maximal Marginal Relevance (MMR) baseline system for text summarization.

Library of state-of-the-art models (PyTorch) for NLP tasks

Extracts key terminology (n-grams) from any large collection of documents (>1000) and forecasts emergence

Abstractive and Extractive Text summarization using Transformers.

Machine Learning and NLP: Text Classification using python, scikit-learn and NLTK

In a nutshell, this is a Text Summarizer

Awesome-Text-Classification Projects,Papers,Tutorial .

An introduction to Natural Language processing using NLTK with python.

A python chatbot framework with Natural Language Understanding and Artificial Intelligence.

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

This repository provides everything to get started with Python for Text Mining / Natural Language Processing (NLP)

API server for NLTK

📖 [译] Python 自然语言处理 中文第二版

My solutions to the projects assigned for the Udacity Computer Vision Nanodegree

📈 Stocks technical analysis code collection and Stocks data platform.

Topic modelling on financial news with Natural Language Processing

Gazeta: Dataset for automatic summarization of Russian news / Газета: набор данных для автоматического реферирования на русском языке

A factoid based question answering system | Python | Flask | NLP

Scripts for an upcoming blog "Extractive vs. Abstractive Summarization" for RaRe Technologies.

Lectures in Urban Data Science Lab, Seoul

An NLP tool which classifies and summarizes resumes

Multilingual automatic text summarizer using statistical approach and extraction

reimplementing Neural Summarization by Extracting Sentences and Words

[NAACL2018] Entity Commonsense Representation for Neural Abstractive Summarization

NLP with NLTK for Sentiment analysis amazon Products Reviews

Extracts character names from a text file and performs analysis of text sentences containing the names.

Python AI assistant 🧠

Multi-Class Text Classification for products based on their description with Machine Learning algorithms and Neural Networks (MLP, CNN, Distilbert).

텐서플로우와 머신러닝으로 시작하는 자연어처리(로지스틱회귀부터 트랜스포머 챗봇까지)

NLP Cheat Sheet, Python, spacy, LexNPL, NLTK, tokenization, stemming, sentence detection, named entity recognition

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].

[AAAI2021] Unsupervised Opinion Summarization with Content Planning

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

A bidirectional encoder-decoder LSTM neural network is trained for text summarization on the cnn/dailymail dataset. (MIT808 project)

NLTK Source

Sentiment analysis and opinion mining of Reddit data.

🍊 📄 Text Mining add-on for Orange3

Extensive tutorials for the Advanced NLP Workshop in Open Data Science Conference Europe 2020. We will leverage machine learning, deep learning and deep transfer learning to learn and solve popular tasks using NLP including NER, Classification, Recommendation \ Information Retrieval, Summarization, Classification, Language Translation, Q&A and T…

Sentiment Analysis on the First Republic Party debate in 2016 based on Python,NLTK and ML.

Document Search Engine Tool

Statistical and Semantical Text Summarizer in Persian Language

TextRank implementation for C#

Russian language support for NLTK's PunktSentenceTokenizer

Text classification with Reuters-21578 datasets using Gensim Word2Vec and Keras LSTM

maximum entropy based part-of-speech tagger for NLTK

A SVM model that classifies the reviews as real or fake. Used both the review text and the additional features contained in the data set to build a model that predicted with over 85% accuracy without using any deep learning techniques.