18564349. ABSTRACTIVE CONTENT TRANSFORMATION simplified abstract (Microsoft Technology Licensing, LLC)

ABSTRACTIVE CONTENT TRANSFORMATION

Organization Name

Microsoft Technology Licensing, LLC

Inventor(s)

Warren A. Aldred of Redmond WA (US)

Si-Qing Chen of Bellevue WA (US)

Rama S. Ganesamoorthy Kasthuri of Sammamish WA (US)

Xun Wang of Amherst MA (US)

Weixin Cai of Kirkland WA (US)

Xinyu He of Lynnwood WA (US)

Xingxing Zhang of Beijing (CN)

Zhang Li of Bellevue WA (US)

Kaushik R. Narayanan of Bellevue WA (US)

Furu Wei of Beijing (CN)

Cheng Yang of Bellevue WA (US)

ABSTRACTIVE CONTENT TRANSFORMATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 18564349 titled 'ABSTRACTIVE CONTENT TRANSFORMATION

The abstract describes a sequence-to-sequence summarizer that determines if the source content meets a size threshold, divides it into sections, generates summaries for each section, and merges them into a document summary for user interaction.

• Simplified Explanation:

A summarizer divides source content into sections and generates summaries for each section, merging them into a document summary for user interaction.

• Key Features and Innovation:

- Sequence-to-sequence summarization technology - Automatic division of content into sections - Generation of summaries for each section - Merging of section summaries into a document summary

• Potential Applications:

- Automated content summarization for various industries - Enhancing user experience in accessing summarized information - Improving efficiency in processing large amounts of text

• Problems Solved:

- Time-consuming manual summarization processes - Difficulty in extracting key information from lengthy content - Inefficient handling of large volumes of text data

• Benefits:

- Increased productivity through automated summarization - Enhanced accessibility to summarized information - Improved decision-making based on concise summaries

• Commercial Applications:

"Automated Content Summarization Technology: Enhancing Information Accessibility and Decision-Making Efficiency"

• Questions about Content Summarization:

1. How does the summarizer determine the size threshold for source content? 2. What are the potential challenges in accurately summarizing technical or specialized content?

• Frequently Updated Research:

Stay updated on advancements in natural language processing and machine learning for improved summarization algorithms.

Original Abstract Submitted

A sequence-to-sequence summarizer receives source content to be summarized and determines whether the source content has a size that meets the size threshold. If so, the source content is divided into sections and the sequence-to-sequence summarizer generates a summary for each section. The summaries for each section are merged into a document summary and surfaced for user interaction.