In today's world, plenty of textual news on stock markets written in different languages are available for traders, financial promoters, and private investors. However, their potential in supporting trading in multiple foreign markets is limited by the large volume of the textual corpora, which is practically unmanageable for manual inspection. Although, text mining and information retrieval techniques allow the automatic generation of interesting summaries from document collections, the study and application of multilingual summarization algorithms to financial news is still an open research problem. This paper addresses the summarization of collections of financial documents written in different languages to enhance the financial actor's awareness of foreign markets. Specifically, the proposed mining system (i) is able to cope with news written in multiple languages, (ii) generates multiple-level summaries covering specific and high-level concepts in separate sections, on behalf of users with different skill levels, and (iii) ranks the summary con- tent based on both objective and subjective quality indices. These features are taking an increasingly important role in financial data summarization. As a case study, a preliminary implementation of the proposed system has been presented and validated on real multilingual news ranging over stocks of different markets. The preliminary results show the effectiveness and usability of the proposed approach.

Supporting stock trading in multiple foreign markets: a multilingual news summarization approach / Baralis, ELENA MARIA; Cagliero, Luca; Cerquitelli, Tania. - STAMPA. - 3:(2016), pp. 1-6. (Intervento presentato al convegno Second International Workshop on Data Science for Macro-Modeling (DSMM@SIGMOD 2016) co-located with the 2016 ACM SIGMOD/PODS Conference tenutosi a San Francisco, CA, USA nel 26 giugno 2016 - 1 luglio 2016) [10.1145/2951894].

Supporting stock trading in multiple foreign markets: a multilingual news summarization approach

BARALIS, ELENA MARIA;CAGLIERO, LUCA;CERQUITELLI, TANIA
2016

Abstract

In today's world, plenty of textual news on stock markets written in different languages are available for traders, financial promoters, and private investors. However, their potential in supporting trading in multiple foreign markets is limited by the large volume of the textual corpora, which is practically unmanageable for manual inspection. Although, text mining and information retrieval techniques allow the automatic generation of interesting summaries from document collections, the study and application of multilingual summarization algorithms to financial news is still an open research problem. This paper addresses the summarization of collections of financial documents written in different languages to enhance the financial actor's awareness of foreign markets. Specifically, the proposed mining system (i) is able to cope with news written in multiple languages, (ii) generates multiple-level summaries covering specific and high-level concepts in separate sections, on behalf of users with different skill levels, and (iii) ranks the summary con- tent based on both objective and subjective quality indices. These features are taking an increasingly important role in financial data summarization. As a case study, a preliminary implementation of the proposed system has been presented and validated on real multilingual news ranging over stocks of different markets. The preliminary results show the effectiveness and usability of the proposed approach.
2016
978-1-4503-4407-4
File in questo prodotto:
File Dimensione Formato  
DSMM-SIGMOD16.pdf

accesso aperto

Descrizione: Versione pre-print (pdf)
Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: PUBBLICO - Tutti i diritti riservati
Dimensione 248.93 kB
Formato Adobe PDF
248.93 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2656998
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo