feat: add abstractive and extractive DL summarizer models (#948)

[PiyushTheProgrammer]

Pull Request for DL-Simplified - Text Summarizer💡

Issue Title : Text Summarizer using Deep Learning

• Info about the related issue (Aim of the project) : The goal of this project is to implement and compare different text summarization techniques. It explores both Abstractive Summarization (using deep learning Transformers to generate human-like summaries) and Extractive Summarization (using statistical NLP algorithms to extract key sentences).
• Name: Piyush Gosavi
• GitHub ID: PiyushTheProgrammer - Email ID: piyushgosavi90@gmail.com - Identify yourself: GSSOC (Girl Script Summer of Code) Contributor Closes: Daily News Text Summarizer using Deep Learning #948

Describe the add-ons or changes you've made 📃

Created a new directory Text Summarizer using DL and added the following:

• Abstractive Models: Implemented T5 (t5-small) and BART (facebook/bart-large-cnn) using the Hugging Face transformers library (PyTorch framework).
• Extractive Models: Implemented TextRank and LSA models using the sumy library and NLTK.
• Documentation: Added a detailed README.md explaining the model architectures, setup instructions, and evaluation insights.
• Dependencies: Added a requirements.txt file (transformers, torch, sumy, nltk, tf-keras) for easy environment setup.

Type of change ☑️

What sort of change have you made:

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Code style update (formatting, local variables)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• This change requires a documentation update

How Has This Been Tested? ⚙️

• Successfully executed the Jupyter Notebook (summarizer_models.ipynb) locally.
• Verified that Hugging Face pipelines (pt framework) successfully download and cache the models.
• Tested both abstractive and extractive generation on a standard sample text, ensuring outputs fit within the max_length and sentences_count parameters without generating any traceback errors.
• Verified that all dependencies install cleanly via requirements.txt.

Checklist: ☑️

• My code follows the guidelines of this project.
• I have performed a self-review of my own code.
• I have commented my code, particularly wherever it was hard to understand.
• I have made corresponding changes to the documentation.
• My changes generate no new warnings.
• I have added things that prove my fix is effective or that my feature works.
• Any dependent changes have been merged and published in downstream modules.

[github-actions]

Our team will soon review your PR. Thanks @PiyushTheProgrammer :)