Pybot can change the way learners try to learn python programming language in a more interactive way. This chatbot will try to solve or provide answer to almost every python related issues or queries that the user is asking for. We are implementing NLP for improving the efficiency of the chatbot. We will include voice feature for more interactivity to the user. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation. NLTK has been called “a wonderful tool for teaching and working in, computational linguistics using Python,” and “an amazing library to play with natural language.The main issue with text data is that it is all in text format (strings). However, the Machine learning algorithms need some sort of numerical feature vector in order to perform the task. So before we start with any NLP project we need to pre-process it to make it ideal for working. Converting the entire text into uppercase or lowercase, so that the algorithm does not treat the same words in different cases as different Tokenization is just the term used to describe the process of converting the normal text strings into a list of tokens i.e words that we actually want. Sentence tokenizer can be used to find the list of sentences and Word tokenizer can be used to find the list of words in strings.Removing Noise i.e everything that isn’t in a standard number or letter.Removing Stop words. Sometimes, some extremely common words which would appear to be of little value in helping select documents matching a user need are excluded from the vocabulary entirely. These words are called stop words.Stemming is the process of reducing inflected (or sometimes derived) words to their stem, base or root form — generally a written word form. Example if we were to stem the following words: “Stems”, “Stemming”, “Stemmed”, “and Stemtization”, the result would be a single word “stem”. A slight variant of stemming is lemmatization. The major difference between these is, that, stemming can often create non-existent words, whereas lemmas are actual words. So, your root stem, meaning the word you end up with, is not something you can just look up in a dictionary, but you can look up a lemma. Examples of Lemmatization are that “run” is a base form for words like “running” or “ran” or that the word “better” and “good” are in the same lemma so they are considered the same.

A super fast walkthrough of NLP Text Summarization with Hugging Face Transformers.

Advanced NLP Workshop: word-sense disambiguation with RoBERTa and text summarization with BART (Machine Learning Milan)

Discussion Summarization is the process of condensing a text document which is a collection of discussion threads, using CBS (Cluster Based Summarization) approach in order to create a relevant summary which enlists most of the important points of the original thematic discussion, thereby providing the users, both concise and comprehensive piece of information. This outlines all the opinions which are described from multiple perspectives in a single document. This summary is completely unbiased as they present information extracted from multiple sources based on a designed algorithm, without any editorial touch or subjective human intervention. Extractive methods used here, follow the technique of selecting a subset of existing words, phrases, or sentences in the original text to form the summary. An iterative ranking algorithm is followed for clustering. The NLP (Natural Language Processing) is used to process human language data. Precisely, it is applied while working with corpora, categorizing text, analyzing linguistic structure. Thus, the quick summary is aimed at being salient, relevant and non-redundant. The proposed model is validated by testing its ability to generate optimal summary of discussions in Yahoo Answers. Results show that the proposed model is able to generate much relevant summary when compared to present summarization techniques.

Code and notebook for text summarization with BERT along with a simple baseline model. Includes a research-backed treatment on the state of transfer learning, pretrained models, NLP metrics, and summarization dataset resources.

Develop a complete text summarization system from scratch, focusing on summarizing complex dialogues using the SAMSum dataset. This project emphasizes professional NLP pipelines, fine-tuning state-of-the-art models like Google Pegasus, and implementing modular Python code for maintainability and scalability.

Multi-feature NLP toolkit built with Streamlit — text summarization, named entity recognition, chunking, and semantic similarity powered by Transformer models. Supports English and Portuguese (PT-BR).

This project seeks to create a comprehensive system for summarising research papers by harnessing the latest advancements in AI and NLP. By merging abstractive text summarization with LLMs and the RAG methodology, we anticipate developing a unique and effective approach to extracting valuable insights from research papers

This project is a text summarization system that leverages word sense disambiguation and pre-trained T5 models. It identifies and summarizes repeating words with different meanings, using spaCy for NLP and an n-gram model for candidate word selection. It compares summaries using Rouge scores and outputs the best one. Written in Python with Flask.

CS224N NLP: Text summarization with Neural Networks Project

A Generalized deep learning-based tool for text summarization, using NLP models to generate concise summaries from large texts. Ideal for document summarization and news aggregation.

This project comparison between the most famous extractive summarization algorithms for Arabic text with NLP and AI.

This is a NLP project for Text Summarization which is built with Flask(RESTapi) and deployed on Heroku(PaaS) using NLTK for summarizing text. Here this app takes your huge paragraphs and gives out the only repeating sentences which are important for you.

This powerful toolkit combines real-time speech recognition with NLP to provide live transcription, sentiment analysis, and text summarization. Perfect for meetings, lectures, and content analysis.

## NLP Tools Flask Application This Flask application serves as a web interface for various Natural Language Processing (NLP) tasks using Hugging Face's Transformers library. The application allows users to interact with different NLP models for text generation, translation, summarization, and more. ### Key Features: - **GPT-3 Text Generation:**

Topsis Best Pre-Trained Model: Advanced NLP using TOPSIS. Optimal for high-quality text aligned with specific criteria. Ideal for document summarization, content recommendation, and decision support. Elevate your text generation tasks on GitHub effortlessly.

Scalable Django DRF backend for advanced NLP: Sentiment Analysis & Text Summarization via API. Features secure JWT auth, user management, history, Redis caching, and Docker. Integrates LLMs (Gemini) with future local AI support

A Python-based tool for generating concise summaries from video, audio, or text inputs using advanced NLP. Built with FastAPI, it supports multi-format inputs, customizable summarization prompts, and integration with LLM clients like OpenRouter and Together. Perfect for quick insights from media! 🚀

Enormous number of video recordings are being created and shared on the Internet through out the day. It has become really difficult to spend time in watching such videos which may have a longer duration than expected and sometimes our efforts may become futile if we couldn't find relevant information out of it. Summarizing transcripts of such videos automatically allows us to quickly look out for the important patterns in the video and helps us to save time and efforts to go through the whole content of the video. This project will give us an opportunity to have hands on experience with state of the art NLP technique for abstractive text summarization and implement an interesting idea suitable for intermediates and a refreshing hobby project for professionals.

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A comprehensive NLP project suite featuring BERT-based implementations for text prediction, summarization, and analysis with modular services and APIs.

NLP pipeline for analyzing Amazon food reviews with sentiment analysis, topic modeling, NER, and text summarization using traditional and transformer-based methods.

IT-Based textgeneration with the use of NLP methods. A text summarization task is conducted with the amazon fine food review dataset from Kaggle. This task is done by attention and lstm neural networks.

This project implements an end-to-end text summarization pipeline using Natural Language Processing (NLP) techniques. The model is deployed using AWS EC2 and AWS ECR with CI/CD automation through GitHub Actions.

An intelligent text summarization tool that supports both extractive and abstractive approaches. It combines traditional NLP (TF-IDF, Logistic Regression, spaCy) with modern deep learning (Hugging Face Transformers) to generate concise and meaningful summaries from long documents.

LexiScribe — A Java-based NLP tool that classifies articles into topics and generates 2–3 sentence summaries. Supports PDF, DOCX, and plain text with offline processing, TF-IDF + Naive Bayes classification, and dual summarization (TextRank & frequency-based).

An end‑to‑end application leveraging Hugging Face pretrained models for multiple NLP and vision tasks—text summarization, next‑word prediction, story generation, chatbot, sentiment analysis, question answering, and image synthesis—with a user‑friendly front end and built‑in performance metrics.

A curated collection of Jupyter notebooks and scripts demonstrating how to load, fine-tune, and deploy transformer-based NLP models with the HuggingFace Transformers library. Includes hands-on examples for text generation, classification, summarization, and more—designed for clarity, reproducibility, and easy extension.

The AI-Text-Summarizer App aims to provide users with an efficient tool for summarizing lengthy textual content into concise and digestible summaries. Leveraging state-of-the-art natural language processing (NLP) algorithms, the app automates the summarization process, saving users valuable time and effort.

Text summarization NLP