Text Generation is a subfield of Natural Language Processing (NLP) which attempts to teach machines to automatically generate texts that are indistinguishable from texts written by humans.

Text Summarization is a subfield of Information Extraction & Text Mining and Text Generation that deals with the automatic generation of a concise and coherent summary of a longer text. It involves developing algorithms and models that can identify the most important information in a text and generate a summary that captures the essence of the original text.

Information Extraction & Text Mining is a subfield of Natural Language Processing (NLP) that focuses on automatically extracting structured information and knowledge from unstructured textual data.

Speech & Audio in NLP is a subfield of Multimodality that deals with the processing of spoken language and audio data in conjunction with textual data. It involves developing algorithms and models that can recognize and transcribe speech and audio, understand and interpret their meaning, and generate natural language responses.

Natural Language Processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of 'understanding' the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.

Multimodality is a subfield of Natural Language Processing (NLP) that refers to the capability of a system or method to process input of different types or “modalities”, such as natural language text, speech, audio, images, video, and programming languages in NLP applications. It involves developing algorithms and models that can process and analyze information from multiple modalities, and integrate them to form a unified representation of the input.

Podcast episodes often contain material extraneous to the main content, such as advertisements, interleaved within the audio and the written descriptions. We present classifiers that leverage both textual and listening patterns in order to detect such content in podcast descriptions and audio transcripts. We demonstrate that our models are effective by evaluating them on the downstream task of podcast summarization and show that we can substantively improve ROUGE scores and reduce the extraneous content generated in the summaries.

Publication:

Detecting Extraneous Content in Podcasts

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