Document Summarization Use Case - Codeq Natural Language Processing API

Document Summarization

Problem

You need to extract the most important information from a large amount of text.

Data sources:

News articles.

Solution

Identify the relevant content and avoid information overload. Codeq's comprehensive offering of summarization and information technology allows you to encapsulate the important sentences and keyphrases of your texts, as well as to detect and disambiguate the most relevant Named Entities.

Our suite of NLP modules can be customized to your needs; we hand-tune the parameters of our solution until our API produces accurate and concise results.

API modules

Text Summarization
Named Entity Recognition
Named Entity Linking
Named Entity Salience
Coreference Resolution
Keyphrase Extraction

Case Study

Next Level News is looking to add a new feature to their application that organizes news by subject, then displays relevant articles from a range of sources that cover the whole political spectrum.

As a key part of this feature, they want to display summaries of the news articles, giving their users a broad view of the information available.

Using our summarization tools, digestible, concise summaries become easily available.
Our named entity modules make it easy to link relevant entities to other subjects, giving Next Level News' users an easy way to stay engaged in the application.

Example:

“ White House chief of staff Mick Mulvaney and the president’s son-in-law and senior White House adviser Jared Kushner are trying to take the lead on the West Wing’s response to the impeachment inquiry, officials said. Mulvaney has led several meetings this week with White House officials on impeachment, which have included Kushner, a key conduit with the president’s re-election campaign. The Trump campaign has rolled out three impeachment-related ads in the last week, all echoing Trump’s language on corruption and accusing Democrats of leading a coup to remove him from office. ”