Creating a Youtube Summariser – Mini NLP Project

Introduction
In recent years, natural language processing (NLP) has emerged as one of the most exciting and fast-growing fields in artificial intelligence and computer science. NLP focuses on enabling computers to understand, interpret, and generate human language in the form of text and speech. With the incredible explosion of digital text data we generate every day, from website content and books to social media posts and messages, NLP holds immense potential to help us extract insights and automate the processing of this vast trove of unstructured data.

Some of the most prominent applications of NLP today include machine translation, chatbots and virtual assistants, sentiment analysis, named entity recognition, text classification, and document summarization. In particular, automatic text summarization is gaining attention as a key NLP capability, with the goal of condensing long text documents into a shorter version that captures the main ideas, while preserving important information and overall meaning. By generating concise summaries, NLP can help us quickly make sense of large amounts of text data, saving us considerable time and effort.

One domain where summarization can be especially useful is video content. Online video has seen astonishing growth in the past decade, with YouTube alone seeing over 500 hours of new video content uploaded every minute, and over 1 billion hours of video watched on the platform every day. With many educational and informational YouTube videos stretching well over an hour in length, it can be difficult and time-consuming to browse through all potentially relevant videos and find the most valuable parts. This is where automatic video summarization can help, by generating a concise overview of the key topics and ideas covered in a video.

In this post, we‘ll walk through how to build a YouTube transcript summarizer using NLP and Python. We‘ll use the YouTube API to obtain the transcript for a given video, and then apply various extractive text summarization techniques to generate a concise summary. While we‘ll be focusing on YouTube, the same approach can be readily applied to any video or audio content that has an associated text transcript.

Our implementation will cover the following key steps:

1. Fetch the transcript for a YouTube video using the YouTube Transcript API
2. Preprocess and clean the transcript text
3. Apply extractive summarization methods to find the most relevant parts of the transcript:

• Using TF-IDF scores to find the top N most representative sentences
• Applying the BART transformer model for abstractive summarization

4. Return the final summarized version of the video transcript

By the end of this post, you‘ll have a working YouTube video summarizer that you can adapt and extend for your own applications. Let‘s dive in!

Obtaining the Video Transcript
The first step is to obtain the transcript text for the YouTube video we want to summarize. YouTube automatically generates closed captions for most videos using speech recognition algorithms. While not perfect, these auto-generated captions provide a reasonable starting point for our summarizer.

To fetch the captions for a given video, we‘ll use the excellent YouTube Transcript API. This API handles the details of making authenticated requests to the YouTube API to retrieve the captions.

First install the youtube_transcript_api package:

!pip install youtube_transcript_api

Then we can use it to fetch the transcript for any YouTube video given its ID:

from youtube_transcript_api import YouTubeTranscriptApi

video_id = "your-youtube-video-id"

transcript = YouTubeTranscriptApi.get_transcript(video_id)

transcript_text = " ".join([entry[‘text‘] for entry in transcript])
print(transcript_text)

This gives us the full video transcript text, which we can then feed into our summarization pipeline.

Preprocessing the Transcript
Before applying our summarization algorithms, it‘s a good idea to preprocess and clean up the raw transcript text. Some common preprocessing steps include:

• Remove special characters and non-alphabetic symbols
• Convert to lowercase
• Break the text into sentences
• Optionally remove very short sentences with little content
• Remove filler words and stop words that don‘t contribute to the core meaning

Here‘s an example of how we can implement these preprocessing steps in Python using the NLTK library:

import re
import nltk
from nltk.corpus import stopwords

VIDEO_ID = "your-youtube-video-id"

# Fetch the video transcript
transcript = YouTubeTranscriptApi.get_transcript(VIDEO_ID)
transcript_text = " ".join([entry[‘text‘] for entry in transcript])

# Remove special characters
transcript_text = re.sub(r"\s+", " ", transcript_text)
transcript_text = re.sub(r"[^a-zA-Z0-9]", " ", transcript_text)  

# Convert to lowercase and split into sentences
sentences = nltk.sent_tokenize(transcript_text.lower())

# Remove short sentences with less than 5 words
sentences = [sent for sent in sentences if len(sent.split()) > 5]

# Remove filler words and common stop words
stop_words = stopwords.words(‘english‘) 
sentences = [" ".join([w for w in s.split() if w not in stop_words]) for s in sentences]

This leaves us with a list of clean, informative sentences extracted from the video transcript, ready for summarization.

Extractive Summarization using TF-IDF
Now onto the core summarization algorithms! There are two main approaches to text summarization:

1. Extractive summarization: selecting the most relevant sentences from the original text to form a summary
2. Abstractive summarization: generating new summary text in the model‘s own words, while capturing the key ideas

Let‘s start with the extractive approach, which is simpler to implement and often works well for short-form content like video transcripts. The intuition behind extractive summarization is that the most representative and informative sentences in a document are the ones containing words that appear frequently in the document itself, but less frequently in the corpus as a whole. We can quantify this intuition using a metric called term frequency-inverse document frequency (TF-IDF).

The TF-IDF score for a word in a document balances two factors:

• The term frequency (TF): how many times the word appears in the document, normalized by the total number of words
• The inverse document frequency (IDF): the log of the total number of documents divided by the number of documents that contain the word

Multiplying these together gives the final TF-IDF score. Words with high TF-IDF scores occur frequently in the given document, but rarely in other documents, suggesting they are particularly important to the meaning of that document.

To apply TF-IDF for extractive summarization, we can represent each sentence as a vector of its constituent words‘ TF-IDF scores. Then, we simply select the top N sentences with the highest total TF-IDF scores to form our summary. Here‘s how it looks in Python using scikit-learn:

from sklearn.feature_extraction.text import TfidfVectorizer

# Convert sentences to TF-IDF vectors
vectorizer = TfidfVectorizer()
tfidf_matrix = vectorizer.fit_transform(sentences)

# Get sentence scores from TF-IDF vectors  
sent_scores = tfidf_matrix.sum(axis=1).ravel()

# Get top N sentences with highest TF-IDF scores
N = 3
top_sents = [sentences[i] for i in np.argsort(sent_scores)[::-1][:N]]

# Assemble final summary from top sentences
summary = " ".join(top_sents)
print("Final summary:")
print(summary)

And there we have it – a simple but effective extractive summary capturing the main points of the video! We can easily tune the output summary length by adjusting the number N of top sentences to return.

Abstractive Summarization with BART
While the extractive TF-IDF approach works quite well, it has some limitations. Since the summary sentences are taken verbatim from the original text, the final summary can sometimes sound a bit choppy and lack coherence. More advanced NLP models can overcome this by generating new summary text from scratch, in a more human-like way. This is known as abstractive summarization.

Some popular choices for abstractive summarization include:

• BART (Bidirectional and Auto-Regressive Transformers), a denoising autoencoder trained to reconstruct original text from corrupted input
• T5 (Text-to-Text Transfer Transformer), an encoder-decoder model pretrained on a multi-task mixture of unsupervised and supervised tasks
• Pegasus, a large Transformer-based encoder-decoder model with pretraining focused specifically on abstractive summarization

For our purposes, let‘s use the BART model, which has shown strong performance on summarization benchmarks like CNN/DailyMail and XSum. We can leverage the Hugging Face Transformers library which provides an easy-to-use implementation of BART and many other state-of-the-art NLP models.

First install the transformers package:

pip install transformers

Then loading a pretrained BART summarization model is as simple as:

from transformers import BartTokenizer, BartForConditionalGeneration

model = BartForConditionalGeneration.from_pretrained(‘facebook/bart-large-cnn‘)
tokenizer = BartTokenizer.from_pretrained(‘facebook/bart-large-cnn‘)

We can then run the model to generate an abstractive summary like this:

# Encode transcript text into model input ids  
input_ids = tokenizer.encode(transcript_text, return_tensors=‘pt‘, max_length=1024)

# Run model to generate summary  
summary_ids = model.generate(input_ids, 
                             num_beams=4,
                             length_penalty=2.0,
                             max_length=142,
                             min_length=56,
                             no_repeat_ngram_size=3)

# Decode summary text
summary_text = tokenizer.decode(summary_ids.squeeze(), skip_special_tokens=True)

print(summary_text)

This generates a smooth, coherent summary in the model‘s own words that reads much more naturally compared to the extractive output. The main downside is that abstractive models like BART are much larger and more computationally intensive than the simple TF-IDF approach. But with the ready availability of large pretrained models, abstractive summarization is becoming increasingly practical for a wide range of applications.

Conclusion
In this post, we demonstrated how to leverage modern NLP techniques to build an effective YouTube video summarizer. The core steps were:

1. Fetch the video transcript using the YouTube API and YouTubeTranscriptApi package
2. Preprocess and clean up the transcript text
3. Apply extractive summarization using TF-IDF sentence scores
4. Apply abstractive summarization using a pretrained BART model

The complete code for this project is available on GitHub here: YouTube Summarizer

With just a few dozen lines of code, we were able to build a practical working summarizer that condenses long videos down to their key points. This approach is easily extendable to other types of video and audio content beyond YouTube. It‘s also worth experimenting with other advanced summarization models like T5 and Pegasus to see how they perform on your target domain.

As NLP continues to evolve at a rapid pace, the capabilities of automatic summarization will only grow. Recent innovations like the ConvSRC model promise near human-level text summarization performance, by pretraining on large dialogue corpora specifically for the summarization task. As models grow in sophistication, potential applications of summarization technology will expand to areas like medical records analysis, financial reporting, legal contract review, and more.

At the same time, as AI systems that can generate highly convincing text from scratch become more prevalent, it will be critical to develop robust safeguards against potential misuse, such as spreading misinformation or impersonating humans. While the conveniences of NLP are exciting, we must proactively work to ensure they are developed and deployed in an ethical and responsible manner.

The field of NLP has made remarkable strides in recent years thanks to more powerful models, ever-growing training datasets, and increased computing power. Tasks that once seemed like science fiction, from machine translation to summarization to open-ended dialogue, are becoming realities. With the vast explosion of textual data in our world today, the opportunities to deliver transformative value through NLP across industries have never been greater.

I hope this deep dive into building a YouTube summarizer has piqued your interest in the fascinating world of NLP and inspired you to experiment with these techniques yourself. What other applications of summarization and NLP are you excited about? Let me know in the comments below!