SummarizeURLContent

Summarize the contents of a provided file or URL

Programmatic Examples

Summarize a web page:

In[1]:=

LLMFunction[ResourceObject["Prompt""SummarizeURLContent"]]["https://www.wolfram.com"]

Out[1]=

Wolfram is a technology company that specializes in computation, data, and decision-making solutions. They offer various products and services, including Wolfram|One, Mathematica, Wolfram|Alpha Notebook Edition, Finance Platform, System Modeler, and Wolfram Player. Wolfram's technologies include the Wolfram Language, Wolfram Notebooks, Wolfram Data Framework, Wolfram Cloud, Wolfram Engine, and Wolfram Universal Deployment System. They provide solutions for engineering, R&D, finance, statistics, business analysis, software development, and education, among other fields. Additionally, they offer learning resources and support services, including Wolfram Language documentation, webinars, training, and technical consulting.

Summarize a notebook:

In[2]:=

LLMFunction[ResourceObject["Prompt""SummarizeURLContent"]]["ExampleData/document.nb"]

Out[2]=

The given file contains various types of content, including:1. Text: A line of text and a subsection with words "one two three" and Greek letters "alpha beta gamma delta epsilon".2. Expressions: Mathematical expressions such as 2+2, the golden ratio, and nested style function with font color and depth.3. Graphics: A 3D sphere graphic and a plot of the absolute value of x in the range -2 to 2.4. Dynamic Objects: A slider for a dynamic value of x and its display using the Dynamic function.

Summarize a PDF file:

In[3]:=

LLMFunction[ResourceObject["Prompt""SummarizeURLContent"]]["https://exampledata.wolfram.com/article.pdf"]

Out[3]=

The paper titled "Cut to Fit: Tailoring the Partitioning to the Computation" by Iacovos Kolokasis and Polyvios Pratikakis discusses optimization of partitioning strategies for social graph analytics applications. The authors evaluate six graph partitioning algorithms on six social graphs using four standard graph algorithms and measure their performance based on five partitioning metrics. They identify communication cost as the best predictor of performance for most analytics computations. The authors also conclude that the optimal partitioning strategy depends on the properties of the graph dataset, the analytics computation, and the number of partitions. These insights can significantly impact the performance of large graph analytics computations and help optimize partitioning strategies for specific datasets and computations.

Prompt Source

Definition Notebook »

Publisher Information

Contributed by: Richard Hennigan (Wolfram Research)