Wouldn't it be great if your computer could do all of that for you: gather the right sources (e.g. paragraphs from relevant Wikipedia pages), synthetize the information, and write up an easy-to-read, original summary of the relevant points? Such a system isn't quite available yet, at least not one that can provide reliable information in its summary. Even though current systems excel at finding an extractive span that answers a factoid question in a given document, they still find open-domain settings where a model needs to find its own sources of information and long answer generation challenging.

Thankfully, a number of recent advances in natural language understanding and generation have made working toward solving this problem much easier! These advances include progress in the pre-training (e.g. BART, T5) and evaluation (e.g. for factuality) of sequence-to-sequence models for conditional text generation, new ways to use language understanding models to find information in Wikipedia (e.g. REALM, DPR), and a new training dataset introduced in the paper ELI5: Long Form Question Answering.

CKAN is open source data management portal software for extremely large data sets, on the scale of entire governments. Used to publish, curate, share, search, and find datasets of just about any kind. Does visualization and metrics. Source code on Github (https://github.com/ckan/ckan). Written in Python, uses Postgres as its back end. Can interface with Apache Solr for search and indexing.