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Poster presentation BIRNDL 2019

The Poli2Sum method is a machine learning- based approach to Identify, Classify and Summarize cited text spans by exploiting the citation context.

publications

Poli2Sum@ CL-SciSumm-19: Identify, Classify, and Summarize Cited Text Spans by means of Ensembles of Supervised Models

Published in In 4th Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2019) @ SIGIR 2019 (Vol. 2414, pp. 233–246), 2019

This paper presents the Poli2Sum approach to the 5th Computational Linguistics Scientific Document Summarization Shared Task (BIRNDL CL-SciSumm 2019).

Recommended citation: La Quatra, M., Cagliero, L., & Baralis, E. (2019). Poli2Sum@CL-SciSumm-19: Identify, Classify, and Summarize Cited Text Spans by means of Ensembles of Supervised Models. In 4th Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2019) @ SIGIR 2019 (Vol. 2414, pp. 233–246). http://ceur-ws.org/Vol-2414/paper24.pdf

Combining Machine Learning and Natural Language Processing for Language-Specific, Multi-Lingual, and Cross-Lingual Text Summarization: A Wide-Ranging Overview

Published in Trends and Applications of Text Summarization Techniques, 2019

The recent advances in multimedia and web-based applications have eased the accessibility to large collections of textual documents. To automate the process of document analysis, the research community has put relevant efforts into extracting short summaries of the document content.

Recommended citation: Cagliero, Luca, Paolo Garza, and Moreno La Quatra. "Combining Machine Learning and Natural Language Processing for Language-Specific, Multi-Lingual, and Cross-Lingual Text Summarization: A Wide-Ranging Overview." Trends and Applications of Text Summarization Techniques. IGI Global, 2020. 1-31. https://www.igi-global.com/chapter/combining-machine-learning-and-natural-language-processing-for-language-specific-multi-lingual-and-cross-lingual-text-summarization/235739

From Hotel Reviews to City Similarities: A Unified Latent-Space Model

Published in Electronics, 9(1), 2020

In the context of hospitality management, a challenging research problem is to identify effective strategies to explain hotel reviews and ratings and their correlation with the urban context. Under this umbrella, the paper investigates the use of sentence-based embedding models to deeply explore the similarities and dissimilarities between cities in terms of the corresponding hotel reviews and the surrounding points of interests.

Recommended citation: Cagliero, L.; La Quatra, M.; Apiletti, D. From Hotel Reviews to City Similarities: A Unified Latent-Space Model. Electronics 2020, 9, 197. https://www.mdpi.com/2079-9292/9/1/197

talks

Using Regression Models to pinpoint Relevant Content in Research Papers

Aggiornato:

Thanks to the world-scale diffusion of web-based applications, digital libraries are playing a foundamental role in giving access to research papers thus allowing researchers to disseminate their main research findings. From the researchers’ perspective, accessing such a huge mass of documents could become critical. they often need to identify the papers that fit their research interests. Our work focuses on extracting the sentences that best summarize the main topics and finding of the research manuscript in an automated manner. To do so we propose a machine learning approach aimed at giving an implicit rank to the sentences according to their informative content. We propose to train regression-based algorithms from a variety of document features in order to relevant text snippets.

teaching

Introduction to databases (Management Engineering)

Teaching assistant for undergraduate course, Politecnico di Torino, DAUIN, 2019

This course is an introduction to databases for undergraduate students of management engineering. The training activities address the following topics:

Introduction to databases (Computer Engineering)

Teaching assistant for undergraduate course, Politecnico di Torino, DAUIN, 2020

This course is an introduction to databases for undergraduate students of computer engineering. The training activities address the following topics: