17986117. KEYPHRASE GENERATION simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)

KEYPHRASE GENERATION

Organization Name

INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor(s)

Md Faisal Mahbub Chowdhury of Woodside NY (US)

Alfio Massimiliano Gliozzo of Brooklyn NY (US)

Gaetano Rossiello of Brooklyn NY (US)

Michael Robert Glass of Bayonne NJ (US)

NANDANA SAMPATH Mihindukulasooriya of Dublin (IE)

KEYPHRASE GENERATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 17986117 titled 'KEYPHRASE GENERATION

Simplified Explanation

The patent application describes a method for generating keyphrases from a document using a trained model and calculating relevance scores for each keyphrase based on their similarity to the document. The relevance scores are then adjusted using a diversity balancing function.

• Trained keyphrase generation model used
• Keyphrases generated based on input document
• Relevance scores calculated for each keyphrase
• Scores adjusted with diversity balancing function

Potential Applications

This technology could be applied in various fields such as information retrieval, document summarization, and search engine optimization.

Problems Solved

This technology helps in automatically generating keyphrases for documents, which can assist in organizing and summarizing large amounts of text efficiently.

Benefits

The keyphrase generation model can save time and effort in manually generating keyphrases for documents, improving productivity and accuracy in information retrieval tasks.

Potential Commercial Applications

One potential commercial application of this technology could be in developing software tools for content creators, researchers, and businesses to automatically generate keyphrases for their documents.

Possible Prior Art

One possible prior art for this technology could be existing methods for keyword extraction and document summarization using natural language processing techniques.

How does this technology compare to existing keyphrase generation models in terms of accuracy and efficiency?

This article does not provide a comparison with existing keyphrase generation models to evaluate its performance.

What are the potential limitations or challenges in implementing this technology in real-world applications?

The article does not discuss any potential limitations or challenges that may arise when implementing this technology in practical settings.

Original Abstract Submitted

Using a trained keyphrase generation model, a set of keyphrases corresponding to an input document is generated, a keyphrase in the set of keyphrases comprising a word summarizing a portion of a document. A relevance score measuring a similarity between the keyphrase and the document is calculated for a keyphrase in the set of keyphrases. The relevance score is adjusted according to a diversity balancing function.