Ranking a stream of news

By G. D. Corso, Antonio Gullì, Francesco Romani. 2005

218 citations recorded by the source corpus.

According to a recent survey made by Nielsen NetRatings, searching on news articles is one of the most important activity online. Indeed, Google, Yahoo, MSN and many others have proposed commercial search engines for indexing news feeds. Despite this commercial interest, no academic research has focused on ranking a stream of news articles and a set of news sources. In this paper, we introduce this problem by proposing a ranking framework which models: (1) the process of generation of a stream of news articles, (2) the news articles clustering by topics, and (3) the evolution of news story over the time. The ranking algorithm proposed ranks news information, finding the most authoritative news sources and identifying the most interesting events in the different categories to which news article belongs. All these ranking measures take in account the time and can be obtained without a predefined sliding window of observation over the stream. The complexity of our algorithm is linear in the number of pieces of news still under consideration at the time of a new posting. This allow a continuous on-line process of ranking. Our ranking framework is validated on a collection of more than 300,000 pieces of news, produced in two months by more then 2000 news sources belonging to 13 different categories (World, U.S, Europe, Sports, Business, etc). This collection is extracted from the index of comeToMyHead, an academic news search engine available online.

Subfields: Information Systems, Statistical and Nonlinear Physics, Information Retrieval & Search, Signal Processing

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Citations in the corpus

Measuring Association Between Labels and Free-Text Rationales (2020, methodology): “The sources of datasets are: CommonsenseQA (Tal-mor et al., 2019), SNLI (Bowman et al., 2015), SST (Socher et al., 2013), AgNews (Del Corso et al., 2005), Evidence Inference (Lehman et al., 2019), Movie Reviews (Zaidan and Eisner, 2008), MultiRC (Khashabi et al., 2018), LGD (Linzen et al., 2016),…”
Joint Embedding of Words and Labels for Text Classification (2018, methodology): “The summary statistics of the data are shown in Table 2, with content speciﬁed below: • AGNews : Topic classiﬁcation over four categories of Internet news articles (Del Corso et al., 2005) composed of titles plus description classiﬁed into: World, Entertainment, Sports and Business.”
Adaptive Regularization of Labels (2019, methodology): “For text classification, we evaluate on AGNews, Yahoo!”
Privacy-preserving Neural Representations of Text (2018, methodology): “News article For topic classiﬁcation of news article, we use two datasets: the AG news corpus (Del Corso et al., 2005) and the English part of the Deutsche Welle (DW) news corpus (Pappas and Popescu-Belis, 2017).”
Learning to Faithfully Rationalize by Construction (2020, unknown): “AgNews (Del Corso et al., 2005).”
Towards recency ranking in web search (2010, background): “[7] proposed the ranking framework to model news article generation, topic clustering and story evolution over time, and this ranking algorithm takes publication time and linkage time into consideration as well as news source authority.”
Aligning Faithful Interpretations with their Social Attribution (2020, background): “AG’s News Corpus (AG News) (Corso et al., 2005) News articles categorized into four topics of Science/Tech, Sports, Business, World.”