Conta-me Histórias / Tell me Stories is a temporal summarization framework of news articles that allows users to explore and revisit events in the past. Built on top of the Portuguese web archive [http://arquivo.pt], it can be extended to support different datasets, including traditional media such as Reuters, Bloomberg, etc. Unlike, the Portuguese version (which runs on top of the Portuguese Web Archive), the English version of this App uses Bing News Search API to create narratives.
During the last decade, we have been witnessing an ever-growing number of online contents posing new challenges for those who aim to understand a given event. This exponential growth of the volume of data, together with the phenomenon of media bias, fake news and filter bubbles, has contributed to the creation of new challenges in information access and transparency. One possible approach to overcome this problem is to automatically summarize large amount of news into consistent narratives. Tell me Stories emerges in this context, as an important contribution for anyone interested in having access to the overall picture of a given event in a quick manner. Our project may be considered as an additional solution that allows the general public (students, journalists, politics, researchers, etc) to better explore any kind of data that has been covered by the media.
Tell me Stories collects information from web archives (such as the Arquivo.pt) or media outlets (such as Reuters, Bloomberg or Bing News) and provides a full-text search service that enables the retrieval of past (to present) information. To select relevant stories of different time-periods, we rely on YAKE! [http://yake.inesctec.pt] a keyword extraction algorithm developed by our research team, which selects the most important excerpts (namely text titles) of a topic over time, to present to the user.
Tell me Stories is available online [http://contamehistorias.pt; http://tellmestories.pt], on Google Play, as an open source Python package [https://github.com/LIAAD/TemporalSummarizationFramework] and as an API.
Requires Python 3
pip install git+https://github.com/LIAAD/TemporalSummarizationFramework.gitUsing ArquivoPT search engine API as datasource.
from contamehistorias.datasources.webarchive import ArquivoPT
from datetime import datetime
# Specify website and time frame to restrict your query
domains = [ 'http://publico.pt/', 'http://www.rtp.pt/',
'http://www.dn.pt/', 'http://news.google.pt/',]
params = { 'domains':domains,
'from':datetime(year=2016, month=3, day=1),
'to':datetime(year=2018, month=1, day=10) }
query = 'Dilma Rousseff'
apt = ArquivoPT()
search_result = apt.getResult(query=query, **params)"search_result" object belongs to the "contamehistorias.datasources.models.ResultHeadLine" class and returns the "raw" results from the Arquivo.pt API.
#Total number of results retrieved by Arquivo.pt
len(search_result)Iterate over the entire set of retrieved results:
for x in search_result:
print(x.datetime)
print(x.domain)
print(x.headline)
print(x.url)
print()
from contamehistorias import engine
language = "pt"
cont = engine.TemporalSummarizationEngine()
summ_result = cont.build_intervals(search_result, language, query)
cont.pprint(summ_result)
Output
-------------------------
2016-03-01 01:22:06 until 2016-03-26 07:03:42
líder da maior associação patronal do brasil pede saída de dilma rousseff
começou processo de impeachment de dilma rousseff
dilma rousseff foi a casa de lula da silva oferecer solidariedade
milhares de pessoas saem à rua contra governo de dilma rousseff
milhares saem à rua exigindo demissão da presidente brasileira dilma rousseff
sociedade civil exige na rua demissão de dilma rousseff
manifestações pressionam dilma rousseff para deixar a presidência
milhares protestam contra governo de dilma rousseff no brasil
lula já terá aceitado ser ministro de dilma rousseff
presidente brasileira confirma entrada de lula da silva para o seu governo
lula da silva é ministro de dilma
lula da silva toma posse como ministro de dilma
dilma rousseff renuncia ao cargo ao nomear lula da silva
partido do ministro do desporto abandona governo de dilma rousseff
oposição a dilma pede destituição do novo ministro da justiça
comissão especial vai analisar a destituição de dilma rousseff
gregório duvivier escreve em exclusivo para a sábado sobre dilma e lula
-------------------------
2016-03-27 04:23:41 until 2016-05-04 07:10:13
parceiro de coligação deixa governo de dilma rousseff
juiz pede desculpa por divulgar escutas de dilma e lula
pmdb oficializa saída do governo de dilma rousseff
lula da silva apoia dilma rousseff através do facebook
lula defende dilma em vídeo no facebook
lula espera que supremo tribunal autorize a sua entrada no governo brasileiro
josé sócrates diz que destituição de dilma é um golpe político
comissão do impeachment aprova parecer favorável à destituição de dilma rousseff
advogado do governo vê irregularidades em parecer favorável à destituição de dilma
deputados aprovam relatório que propõe impeachment de dilma
aprovado relatório que propõe a destituição de dilma rousseff
comissão parlamentar aprova processo de destituição de dilma rousseff
pedido de destituição de dilma rousseff aquece congresso brasileiro
câmara dos deputados vota pedido de afastamento de dilma
câmara dos deputados aprova impeachment de dilma rousseff
parlamento do brasil aprova destituição de dilma
temer assume presidência enquanto rousseff procura apoios na onu
nicolás maduro sai em defesa de dilma rousseff
senado brasileiro aprovou nomes dos parlamentares que vão analisar destituição de dilma
processo de impeachment de dilma já está na comissão especial no senado
"summ_result" is a dictionary with 3 keys (stats, domains, results). "stats" is itself a dictionary; "domains" is a list and "results" is a list with several elements (namely dictionaries with detailed results).
"stats" is a dictionary consisting of 4 keys.
summ_result["stats"]Domains keeps the list of URIs in the search.
summ_result["domains"]summ_result ["results"] is a list that will have as many elements as there are relevant periods. Each of these relevant periods has a dictionary "from" (date) "to" (date) which enables to delimit the boundaries of the time-frame. Each time-period, in turn, has a number of keyphrases/headlines and corresponding information such as, the headline itself, its publication datetime (that is, the date when the webpage was collected by the Arquivo.pt), it's domain and URL.
summ_result = cont.build_intervals(search_result, language, query)
for period in summ_result["results"]:
print("--------------------------------")
print(period["from"],"until",period["to"])
# selected keyphrases
keyphrases = period["keyphrases"]
for keyphrase in keyphrases:
print(keyphrase.kw)
# sources
for headline in keyphrase.headlines:
print("Date", headline.info.datetime)
print("Source", headline.info.domain)
print("Url", headline.info.url)
print()
In case you want to have information about the score of the terms that compose the headline, please run the following code:
for period in summ_result["results"]:
print("--------------------------------")
print(period["from"],"until",period["to"])
# selected headlines
keyphrases = period["keyphrases"]
for keyphrase in keyphrases:
print("headline = " + keyphrase.kw)
# sources
for headline in keyphrase.headlines:
print("Date", headline.info.datetime)
print("Source", headline.info.domain)
print("Url", headline.info.url)
print("Headline completa = ", headline.info.headline)
#Permite imprimir a importância de cada termo que faz parte da headline
for termos in keyphrase.cand_obj.terms:
print(f'{termos.unique_term} {termos.H}')
print() Serializing results. Useful for caching.
from contamehistorias.datasources.webarchive import ArquivoPT
from datetime import datetime
# Specify website and time frame to restrict your query
domains = [ 'http://publico.pt/', 'http://www.rtp.pt/', 'http://www.dn.pt/', 'http://news.google.pt/',]
params = { 'domains':domains, 'from':datetime(year=2016, month=3, day=1), 'to':datetime(year=2018, month=1, day=10) }
query = 'Dilma Rousseff'
apt = ArquivoPT()
search_result = apt.getResult(query=query, **params)
import json
# object to string
search_result_serialized = apt.toStr(search_result)
# string to object
search_result = apt.toObj( search_result_serialized )
print(search_result_serialized)from contamehistorias import engine
import datetime
language = "pt"
cont = engine.TemporalSummarizationEngine()
summ_result = cont.build_intervals(search_result, language, query)
import json
# object to string
summ_result_serialized = json.dumps(cont.serialize(summ_result))
# string to object
summ_result = json.loads(str(summ_result_serialized))print(summ_result_serialized)print(summ_result)You can extend Tell me Stories to use your own data source. All you need to do is to extend BaseDataSource class. See the below code for informative purposes (don't need to execute it):
class BaseDataSource(object):
def __init__(self, name):
self.name = name
def getResult(self, query, **kwargs):
## must returl list of ResultHeadLine(headline, datetime, domain, url)
raise NotImplementedError('getResult on ' + self.name + ' not implemented yet!')
def toStr(self, list_of_headlines_obj):
return json.dumps(list(map(lambda obj: obj.encoder(), list_of_headlines_obj)))
def toObj(self, list_of_headlines_str):
return [ ResultHeadLine.decoder(x) for x in json.loads(list_of_headlines_str) ]Take a look at the example using the SignalMedia dataset, which is indexed at a solr machine. Method getResult must return a list of object ResultHeadLine.
from contamehistorias.datasources import models
from contamehistorias.datasources import utils
from datetime import datetime
import requests
class CustomSignalNewsIRDataset(models.BaseDataSource):
URL_REQUEST = 'http://194.117.29.148/solr/signalnews-articles/select'
def __init__(self, processes=4):
models.BaseDataSource.__init__(self, 'SignalNewsIRDataset')
self.processes = processes
def getResult(self, query, **kwargs):
params = {
'q':'title:(%s) OR content:(%s)' % (query, query),
'rows':2000,
'fl': 'source,title,published'
}
response = requests.get(CustomSignalNewsIRDataset.URL_REQUEST, params=params)
if response.status_code != 200:
return None
response_json = response.json()
results = []
for item in response_json['response']['docs']:
if not (item['title'][0]):
continue
domain_url = item["source"][0]
pubdate = datetime.strptime(item["published"][0], '%Y-%m-%dT%H:%M:%SZ')
title = item['title'][0]
item_result = models.ResultHeadLine(headline=title, datetime=pubdate, domain=domain_url, url="")
results.append( item_result )
return resultsNow run the following code to get the results:
from contamehistorias.datasources import models
from contamehistorias import engine
language = "en"
query = "Donald Trump"
search_result = CustomSignalNewsIRDataset().getResult(query=query)
cont = engine.TemporalSummarizationEngine()
summ_result = cont.build_intervals(search_result, language, query)
cont.pprint(summ_result,True)Given the previous code (which was shown for illustrative purposes) is already part of the package, you can use, instead, the following code to interact with the signal media dataset in a similar fashion as what was done for the Arquivo.pt API. Note that given Signal media only covers one month no time-frame is specified.
from contamehistorias.datasources.signal import SignalNewsIRDataset
from contamehistorias.engine import TemporalSummarizationEngine
from datetime import datetime
query = 'Dilma Rousseff'
language = 'en'
signal = SignalNewsIRDataset()
search_result = signal.getResult(query=query)Likewise the Arquivo.pt, one can interact with the raw results retrieved from the solr machine:
len(search_result)for x in search_result:
print(x.datetime)
print(x.domain)
print(x.headline)
print(x.url)
print()As well as from the relevant headlines:
cont = TemporalSummarizationEngine()
summ_result = cont.build_intervals(search_result, language, query)
cont.pprint(summ_result,True)From the cli:
$ contamehistorias_signal --help
Usage: contamehistorias_signal [OPTIONS]
Console script for tempsummarization.
Options:
--query TEXT Perform news retrieval with informed query
--verbose
--help Show this message and exit.
from contamehistorias.datasources.bing import BingNewsSearchAPI
from contamehistorias.engine import TemporalSummarizationEngine
from datetime import datetime
language = "en"
query = 'Brexit'
key = "Introduce your KEY (from Microsoft Azure)"
bing = BingNewsSearchAPI(key)
search_result = bing.getResult(query=query)Accessing client help
Usage: contamehistorias [OPTIONS]
Options:
--query TEXT Perform news retrieval with informed query
--language TEXT Expected language in headlines
--start_date DATE Perform news retrieval since this date
--end_date DATE Perform news retrieval until this date
--domains TEXT Comma separated list of domains (www.publico.pt,www.dn.pt)
--verbose Gets detailed information
--help Show this message and exit.Running your query. You can specify start_date and end_date using date format (dd/mm/YYYY).
> contamehistorias --query "Donald Trump"
2010-04-12 16:37:09 until 2011-05-19 00:23:39
filha de donald trump está grávida
quanto vale donald trump
donald trump deverá anunciar candidatura à casa branca
2011-07-19 16:24:20 until 2015-11-21 19:54:41
donald trump vai apoiar mitt romney nas primárias
multimilionário donald trump apoia candidatura de mitt romney
multimilionário donald trump anuncia candidatura à corrida presidencial norte-americana
2015-11-22 19:02:15 until 2016-09-30 01:20:27
donald trump quer proibir entrada de muçulmanos nos eua
obama graceja sobre possível discurso do estado da união de donald trump
deputados britânicos discutiram proibição de entrada donald trump no reino unido
2015-11-22 19:02:15 until 2016-09-30 01:20:27
donald trump quer proibir entrada de muçulmanos nos eua
obama graceja sobre possível discurso do estado da união de donald trump
deputados britânicos discutiram proibição de entrada donald trump no reino unido[email protected] [email protected] [email protected]
Developed by researchers from Laboratório de Inteligência Artificial e Apoio a Decisão (LIAAD - INESCTEC), and affiliated to the Universidade da Beira Interior; Universidade do Porto; Universidade de Kyoto
Pasquali, A., Mangaravite, V., Campos, R., Jorge, A., and Jatowt, A. (2019). Interactive System for Automatically Generating Temporal Narratives. In: Azzopardi L., Stein B., Fuhr N., Mayr P., Hauff C., Hiemstra D. (eds), Advances in Information Retrieval. ECIR'19 (Cologne, Germany. April 14 – 18). Lecture Notes in Computer Science, vol 11438, pp. 251 - 255. Springer. pdf
Conta-me Histórias / Tell me Stories won the Arquivo.pt 2018 contest [https://sobre.arquivo.pt/en/arquivo-pt-2018-award-winners/] and has been awarded the Best Demo Presentation at ECIR’19 [http://www.ecir2019.org].