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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

Exploiting pivot words to classify and summarize discourse facets of scientific papers

Published in Scientometrics (2020), 2020

This paper proposes a new, more effective solution to the CL-SciSumm discourse facet classification task, which entails identifying for each cited text span what facet of the paper it belongs to from a predefined set of facets.

Recommended citation: La Quatra, M., Cagliero, L. & Baralis, E. Exploiting pivot words to classify and summarize discourse facets of scientific papers. Scientometrics (2020). https://doi.org/10.1007/s11192-020-03532-3 https://doi.org/10.1007/s11192-020-03532-3

Extracting Highlights of Scientific Articles: a Supervised Summarization Approach

Published in Expert Systems With Applications (2020), 2020

This paper presents a supervised approach, based on regression techniques, with the twofold aim at automatically extracting highlights of past articles with missing annotations and simplifying the process of manually annotating new articles.

Recommended citation: Cagliero L. & La Quatra M., Extracting Highlights of Scientific Articles: a Supervised Summarization Approach, Expert Systems with Applications, 2020, 113659, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2020.113659. https://doi.org/10.1016/j.eswa.2020.113659

End-to-end Training For Financial Report Summarization

Published in 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, 2020

The proposed methodology exploit the advancements in the Natural Language Understanding field to create a fine-tuned architecture able to summarize financial documents.

Recommended citation: La Quatra, M., & Cagliero, L. (2020, December). End-to-end Training For Financial Report Summarization. In Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation (pp. 118-123). https://www.aclweb.org/anthology/2020.fnp-1.20/

talks

Poster presentation BIRNDL 2019

Aggiornato:

Supervised models are trained on a variety of data features related to the structure, semantics and syntax of the text. The idea behind is to effectively explore the latent connections between citing context and sentences in the reference paper.

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. Our work focuses on extracting the sentences that best summarize the main topics and finding of the research manuscript in an automated manner.

Poster presentation @ FNS 2020

Aggiornato:

The summarization architecture proposed for the FNS 2020 shared task is based on a three-phases process.

  • Preprocessing step: clean input financial reports and annotate its content at sentence level.
  • Training step: deep learning models are fine-tuned for the regression task exploiting the annotations obtained during the preprocessing step.
  • Evaluation phase: is applied at document level. The sentences of each annual reports make a forward pass through the fine-tuned model to obtain the estimated relevance score. The final summary merges sentences according to the relevance score predicted by the fine-tuned architecture.

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: