Research Paper on Abstractive Text Summarization, and Challenges

Research Paper on Abstractive Text Summarization, and Challenges

Author¹ Dr. Nilesh T.Gole, studycentre31@gmail.com

Author²  Yogendra Nikam  yashnikam1111@gmail.com

Department of Computer Science &Engineering

Vidarbha Institute of Technology

Abstract -Abstractive text summarization is an important part of language processing create short summaries and integrate long documents. However, one of the big ones

The challenges faced in this domain are handling of word out of words (OOV) words and phrases, which is content not included in the content model table. OOV problem can be for suboptimal content, as they are often incomplete or misrepresented the text. This research aims to address the OOV issues in the critical literature by new ideas and methods. We plan a way that combines pre-processing, model architecture, and post-processing techniques to reduce OOV-related challenges. First, we check the process to have effective OOV detection and mapping, allow us Identify the OOV terms in the text and replace them with their closest equivalent words competitors. Next, we improve the model's expansion model by combining it techniques such as sub-word tokenization and character-level modeling, leading to the model to manage various OOV content during the recording process. Furthermore, we explore the utilization of external knowledge sources, such as domain- specific ontologies and pre-trained language models, to aid in the generation of accurate summaries containing OOV terms. Additionally, we introduce a novel post-processing step that refines the generated summaries by addressing OOV issues and ensuring linguistic fluency.

Key Words: OOV: Out-of-Vocabulary, NLP: Natural Language Processing