Authors :
Palaparthi Manichandana; Nekkanti Durga Sri Jahnavi; Arepalli Shakeena; Sontim Lakshmi Meghana; Bandaru Neha; Dr. Shirin Bhanu koduri
Volume/Issue :
Volume 8 - 2023, Issue 12 - December
Google Scholar :
http://tinyurl.com/mv6vb5md
Scribd :
http://tinyurl.com/4uvkdhkb
DOI :
https://doi.org/10.5281/zenodo.10377051
Abstract :
With the ever-increasing volume of
information on the internet, it becomes imperative to
develop methods to efficiently curate this data for users.
Our overarching goal in this endeavor is to discover an
optimal solution for the task of 'Text Summarization'.
One of the primary challenges encountered in the realm
of text summarization involves the delicate equilibrium
between brevity and informativeness. An effective
summary must adeptly encapsulate the central concepts
and pivotal details of the original text while maintaining
conciseness and logical coherence. In essence, text
summarization occupies a crucial role in addressing the
mounting deluge of textual data witnessed in the digital
era. Its potential is noteworthy, offering the capacity to
economize time, enhance information retrieval, and
amplify knowledge accessibility across diverse domains.
Text summarization entails the process of identifying and
extracting the most pertinent and meaningful
information from a document or a collection of related
documents, subsequently condensing this content into a
concise version while retaining its overarching
significance. In this project, our aim is to assist users in
efficiently summarizing various forms of text data,
including articles, paragraphs, web pages, and blogs, all
within a shorter time frame. In this process, we will
utilize Python within the field of 'Natural Language
Processing' (NLP) along with the 'Natural Language
Toolkit Library' and other Python-based machine
learning libraries. NLP is a domain at the intersection of
computer science and linguistics, focused on facilitating
meaningful interactions between machines and human
languages. We currently reside in an age dominated by
machines, data abundance, and artificial intelligence.
Our aim is to provide output in two formats: text and
voice-based, enabling users to save valuable time while
accessing the information they require.
Keywords :
Transformers, Encoder, Decoder, BART, Self- Attention, Pytssx3, Beautiful Soup4.
With the ever-increasing volume of
information on the internet, it becomes imperative to
develop methods to efficiently curate this data for users.
Our overarching goal in this endeavor is to discover an
optimal solution for the task of 'Text Summarization'.
One of the primary challenges encountered in the realm
of text summarization involves the delicate equilibrium
between brevity and informativeness. An effective
summary must adeptly encapsulate the central concepts
and pivotal details of the original text while maintaining
conciseness and logical coherence. In essence, text
summarization occupies a crucial role in addressing the
mounting deluge of textual data witnessed in the digital
era. Its potential is noteworthy, offering the capacity to
economize time, enhance information retrieval, and
amplify knowledge accessibility across diverse domains.
Text summarization entails the process of identifying and
extracting the most pertinent and meaningful
information from a document or a collection of related
documents, subsequently condensing this content into a
concise version while retaining its overarching
significance. In this project, our aim is to assist users in
efficiently summarizing various forms of text data,
including articles, paragraphs, web pages, and blogs, all
within a shorter time frame. In this process, we will
utilize Python within the field of 'Natural Language
Processing' (NLP) along with the 'Natural Language
Toolkit Library' and other Python-based machine
learning libraries. NLP is a domain at the intersection of
computer science and linguistics, focused on facilitating
meaningful interactions between machines and human
languages. We currently reside in an age dominated by
machines, data abundance, and artificial intelligence.
Our aim is to provide output in two formats: text and
voice-based, enabling users to save valuable time while
accessing the information they require.
Keywords :
Transformers, Encoder, Decoder, BART, Self- Attention, Pytssx3, Beautiful Soup4.
