Nikiforos Pittaras


2020

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Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation
Dr Mahmoud El-Haj | Dr Vasiliki Athanasakou | Dr Sira Ferradans | Dr Catherine Salzedo | Dr Ans Elhag | Dr Houda Bouamor | Dr Marina Litvak | Dr Paul Rayson | Dr George Giannakopoulos | Nikiforos Pittaras
Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation

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The Financial Narrative Summarisation Shared Task (FNS 2020)
Mahmoud El-Haj | Ahmed AbuRa’ed | Marina Litvak | Nikiforos Pittaras | George Giannakopoulos
Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation

This paper presents the results and findings of the Financial Narrative Summarisation shared task (FNS 2020) on summarising UK annual reports. The shared task was organised as part of the 1st Financial Narrative Processing and Financial Narrative Summarisation Workshop (FNP-FNS 2020). The shared task included one main task which is the use of either abstractive or extractive summarisation methodologies and techniques to automatically summarise UK financial annual reports. FNS summarisation shared task is the first to target financial annual reports. The data for the shared task was created and collected from publicly available UK annual reports published by firms listed on the London Stock Exchange (LSE). A total number of 24 systems from 9 different teams participated in the shared task. In addition we had 2 baseline summarisers and additional 2 topline summarisers to help evaluate and compare against the results of the participants.

2019

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The Summary Evaluation Task in the MultiLing - RANLP 2019 Workshop
George Giannakopoulos | Nikiforos Pittaras
Proceedings of the Workshop MultiLing 2019: Summarization Across Languages, Genres and Sources

This report covers the summarization evaluation task, proposed to the summarization community via the MultiLing 2019 Workshop of the RANLP 2019 conference. The task aims to encourage the development of automatic summarization evaluation methods closely aligned with manual, human-authored summary grades and judgements. A multilingual setting is adopted, building upon a corpus of Wikinews articles across 6 languages (English, Arabic, Romanian, Greek, Spanish and Czech). The evaluation utilizes human (golden) and machine-generated (peer) summaries, which have been assigned human evaluation scores from previous MultiLing tasks. Using these resources, the original corpus is augmented with synthetic data, combining summary texts under three different strategies (reorder, merge and replace), each engineered to introduce noise in the summary in a controlled and quantifiable way. We estimate that the utilization of such data can extract and highlight useful attributes of summary quality estimation, aiding the creation of data-driven automatic methods with an increased correlation to human summary evaluations across domains and languages. This paper provides a brief description of the summary evaluation task, the data generation protocol and the resources made available by the MultiLing community, towards improving automatic summarization evaluation.

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A topic-based sentence representation for extractive text summarization
Nikolaos Gialitsis | Nikiforos Pittaras | Panagiotis Stamatopoulos
Proceedings of the Workshop MultiLing 2019: Summarization Across Languages, Genres and Sources

In this study, we examine the effect of probabilistic topic model-based word representations, on sentence-based extractive summarization. We formulate the task of summary extraction as a binary classification problem, and we test a variety of machine learning algorithms, exploring a range of different settings. An wide experimental evaluation on the MultiLing 2015 MSS dataset illustrates that topic-based representations can prove beneficial to the extractive summarization process in terms of F1, ROUGE-L and ROUGE-W scores, compared to a TF-IDF baseline, with QDA-based analysis providing the best results.

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A study of semantic augmentation of word embeddings for extractive summarization
Nikiforos Pittaras | Vangelis Karkaletsis
Proceedings of the Workshop MultiLing 2019: Summarization Across Languages, Genres and Sources

In this study we examine the effect of semantic augmentation approaches on extractive text summarization. Wordnet hypernym relations are used to extract term-frequency concept information, subsequently concatenated to sentence-level representations produced by aggregated deep neural word embeddings. Multiple dimensionality reduction techniques and combination strategies are examined via feature transformation and clustering methods. An experimental evaluation on the MultiLing 2015 MSS dataset illustrates that semantic information can introduce benefits to the extractive summarization process in terms of F1, ROUGE-1 and ROUGE-2 scores, with LSA-based post-processing introducing the largest improvements.