Methods for Mining and Summarizing Text Conversations by Giuseppe Carenini, Gabriel Murray, Raymond Ng

By Giuseppe Carenini, Gabriel Murray, Raymond Ng

As a result of net Revolution, human conversational information -- in written types -- are gathering at a beautiful fee. even as, advancements in speech know-how permit many spoken conversations to be transcribed. contributors and businesses interact in e-mail exchanges, face-to-face conferences, running a blog, texting and different social media actions. The advances in typical language processing supply abundant possibilities for those "informal records" to be analyzed and mined, therefore developing quite a few new and worthy functions. This publication provides a suite of computational how you can extract details from conversational information, and to supply usual language summaries of the knowledge. The e-book starts off with an summary of uncomplicated suggestions, akin to the diversities among extractive and abstractive summaries, and metrics for comparing the effectiveness of summarization and numerous extraction projects. It additionally describes a number of the benchmark corpora utilized in the literature. The e-book introduces extraction and mining equipment for appearing subjectivity and sentiment detection, subject segmentation and modeling, and the extraction of conversational constitution. It additionally describes frameworks for undertaking discussion act reputation, determination and motion merchandise detection, and extraction of thread constitution. there's a particular specialise in appearing these kinds of initiatives on conversational information, equivalent to assembly transcripts (which exemplify synchronous conversations) and emails (which exemplify asynchronous conversations). Very contemporary techniques to house blogs, dialogue boards and microblogs (e.g., Twitter) also are mentioned. the second one 1/2 this ebook makes a speciality of average language summarization of conversational facts. It provides an outline of numerous extractive and abstractive summarizers built for emails, conferences, blogs and boards. It additionally describes makes an attempt for construction multi-modal summarizers. final yet no longer least, the publication concludes with concepts on subject matters for extra improvement. desk of Contents: advent / history: Corpora and evaluate equipment / Mining textual content Conversations / Summarizing textual content Conversations / Conclusions / ultimate options

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In Chapter 4, we will see that similar approaches can be applied to text conversations. 9. Summarization evaluation As with all mining and retrieval tasks, it is critical to have dependable summarization evaluation metrics to assess various systems. It is also important to have widely used evaluation schemes so that researchers can compare results directly with one another and determine the state of the art. In recent years, several approaches to evaluation have become popular within the summarization community and adopted for periodic benchmark tasks.

Topic models can be flat or hierarchical. html topic segmentation topic labeling hierarchical topics 44 3. 1: Sample human generated topic model of an article on the exploration of Venus by the Magellan space probe. The reader split the document into ten segments. For each segment, the numeric range indicates the article paragraphs comprising that segment, while the label specifies the reader description for the segment. Article Paragraphs Reader Description for the Segment 1-2 Intro to Magellan space probe 3-4 Intro to Venus 5-7 Lack of craters 8-11 Evidence of volcanic action 12-15 River Styx 16-18 Crustal spreading 19-21 Recent volcanism 22-23 Future of Magellan further divided into subtopics.

With a reading comprehension task, a user is given either a full source or a summary text and is then given a multiple-choice test relating to information from the full source. One can then compare how well users perform in term of the quality of their answers and the amount of time to produce them, when given only the summary compared with the full source document. This evaluation framework relies on the assumption that truly informative summaries should be able to act as substitutes for the full source document.

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