Installation Instructions
Newsroom requires Python 3 and can be installed using pip
:
pip install -e git+git://github.com/clic-lab/newsroom.git#egg=newsroom
Data Processing Tools
Newsroom contains two scripts for downloading and processing data downloaded from Archive.org. First, download the "thin" data from summari.es:
wget https://summari.es/files/thin.tar
tar xvf thin.tar
Both the newsroom-scrape
and newsroom-extract
tools described below have
argument help pages accessed with the --help
command line option.
Data Scraping
The thin
directory will contain three files, train.jsonl.gz
, dev.jsonl.gz
and test.jsonl.gz
. To begin downloading the development set from Archive.org,
run the following:
newsroom-scrape --thin thin/dev.jsonl.gz --archive dev.archive
Estimated download time is indicated with a progress bar. If errors occur during downloading, you may need to re-run the script later to capture the missing articles. This process is network bound and depends mostly on Archive.org, save your CPU cycles for the extraction stage!
The downloading process can be stopped at any time with Control-C
and resumed
later. It is also possible to perform extraction of a partially downloaded
dataset with newsroom-extract
before continuing to download the full version.
Data Extraction
The newsroom-extract
tool extracts summaries and article text from the data
downloaded by newsroom-scrape
. This tool produces a new file that does not
modify the original output file of newsroom-scrape
, and can be run with:
newsroom-extract --archive dev.archive --dataset dev.data
The script automatically parallelizes extraction across your CPU cores. To
disable this or reduce the number of cores used, use the --workers
option.
Like scraping, the extraction process can be stopped at any point with
Control-C
and resumed later.
Reading and Analyzing the Data
All data are represented using gzip-compressed JSON lines. The Newsroom
package provides an easy tool to read an write these files — and do so up to
20x faster than the standard Python gz
and json
packages!
from newsroom import jsonl
# Read entire file:
with jsonl.open("train.data", gzip = True) as train_file:
train = train_file.read()
# Read file entry by entry:
with jsonl.open("train.data", gzip = True) as train_file:
for entry in train_file:
print(entry["summary"], entry["text"])
Extraction Analysis
The Newsroom package also contains scripts for identifying extractive fragments and computing metrics described in the paper: coverage, density, and compression.
import random
from newsroom import jsonl
from newsroom.analyze import Fragments
with jsonl.open("train.data", gzip = True) as train_file:
train = train_file.read()
# Compute stats on random training example:
entry = random.choice(train)
summary, text = train[0]["summary"], train[0]["text"]
fragments = Fragments(summary, text)
# Print paper metrics:
print("Coverage:", fragments.coverage())
print("Density:", fragments.density())
print("Compression:", fragments.compression())
# Extractive fragments oracle:
print("List of extractive fragments:")
print(fragments.strings())
Evaluation Tools
Available soon!