This Audio lectures and speeches contain a wealth of valuable information, but reviewing and extracting the key points can be tedious and time- consuming. This paper presents an automated system that uses speech recognition and text summarization techniques to identify and summarize the most salient content from spoken presentations. Audio is first transcribed to text via a speech recognition engine. The resulting text is then processed by an extractive summarization algorithm based on term frequency- inverse document frequency (TF-IDF) to extract the most important points. These summarized points can optionally be used to generate relevant supplementary URLs that provide additional context or resources related to the topics covered. This system was developed to enable quick review of lectures and speeches by automatically delivering condensed, relevant summaries.

Keywords : Speech Recognition, Extractive Summarization, TF-IDF, URLs.