Abstract:

This project explores the automatic summarization of classic poems using advanced natural language processing (NLP) techniques. The objective is to develop a model capable of generating concise and accurate summaries of well-known poetic works, preserving the original themes, emotions, and stylistic elements. The poems selected for this study include canonical works such as William Wordsworth’s "Daffodils," Edgar Allan Poe’s "The Raven," and Percy Bysshe Shelley’s "Ozymandias," among others. By employing state-of-the-art algorithms and deep learning models, the system aims to understand and distill complex poetic structures and meanings into simplified yet meaningful summaries. This project not only highlights the challenges inherent in summarizing poetry, such as maintaining the integrity of figurative language and meter, but also contributes to the broader field of literary analysis and digital humanities by providing tools for enhancing the accessibility and comprehension of classic literary texts.
Keywords: NLP(Natural language processing),Token frequency vectorizer(TFV),Machine learning(ML),chatbot

INTRODUCTION:

Poetry, with its intricate use of language, symbolism, and emotion, represents one of the most challenging forms of literature to interpret and summarize. Unlike prose, poems often convey profound meanings and evoke strong emotions through condensed and metaphorical expressions, making them both powerful and complex. The task of summarizing a poem requires a deep understanding not only of its literal content but also of the nuanced themes, tones, and stylistic choices employed by the poet.
This project aims to address the challenge of poem summarization by leveraging advancements in natural language processing (NLP) and machine learning. By focusing on classic poems such as William Wordsworth's "Daffodils," Edgar Allan Poe's "The Raven," and Percy Bysshe Shelley's "Ozymandias," the project seeks to develop a model that can generate coherent and accurate summaries while preserving the essence of the original works. The significance of this endeavor lies in its potential to make complex poetic texts more accessible to a broader audience, fostering greater appreciation and understanding of literary art. Moreover, it contributes to the growing field of digital humanities, where technology and literature intersect to enhance the study and enjoyment of classic works.
The complexity of summarizing poetry arises not only from the condensed and often ambiguous nature of poetic language but also from the diverse structural elements that define each poem. Rhyme schemes, meter, and the use of literary devices such as alliteration, metaphor, and symbolism contribute to the multi-layered meaning of a poem. Traditional summarization techniques, typically designed for prose, often struggle to capture these nuances. Therefore, developing an effective summarization model for poetry requires a specialized approach that can interpret and condense these elements without losing the original poem's emotional and aesthetic impact.
This project incorporates deep learning models, such as transformer-based architectures, that have shown remarkable success in understanding and generating natural language. By training these models on a curated dataset of classic poems, the project aims to create a system capable of producing summaries that reflect both the content and the artistic qualities of the original texts. Through this process, the project seeks to bridge the gap between computational analysis and literary interpretation, providing a tool that can assist students, scholars, and poetry enthusiasts in engaging with classic literature in a more accessible and insightful manner.