Elevating the Precision of Summarization for Short Text in Social Media using Preprocessing Techniques

The prevalence of social media platforms has resulted in an overflow of concise textual content, thereby posing challenges in the accurate summarization of informal and noisy data. Automated text summarization entails the mechanical conversion of lengthy text passages into succinct, coherent summaries. In this study, we delve into the amalgamation of automated text summarization and preprocessing, harnessing natural language processing (NLP) techniques to amplify text quality, minimize noise, and enhance the retrieval of essential insights. This symbiotic integration of summarization and preprocessing aims to streamline the intricacies of text comprehension, thereby expediting data summarization, including real-time sentiment analysis. The study employs evaluation metrics to compute and juxtapose results, providing insights into the impact of preprocessing on the summarization of short texts. Notably, the findings accentuate the elevated effectiveness of text summarization when fortified by pre-processing techniques. This research highlights the pivotal role of preprocessing in elevating the influence and efficiency of text summarization processes.