Supervisors info:
Δημήτριος Γαλάνης, Ερευνητής Γ', Ερευνητικό κέντρο "Αθηνά", Ινστιτούτο Επεξεργασίας του Λόγου (ΙΕΛ)
Αικατερίνη Γκίρτζου, Επιστημονική Συνεργάτιδα, Ερευνητικό κέντρο "Αθηνά", Ινστιτούτο Επεξεργασίας του Λόγου (ΙΕΛ)
Σωκράτης Σοφιανόπουλος, Επιστημονικός Συνεργάτης, Ερευνητικό κέντρο "Αθηνά", Ινστιτούτο Επεξεργασίας του Λόγου (ΙΕΛ)
Summary:
Identifying key statements in large volumes of opinionated texts that appear daily in
social media, and online debates is an essential tool for informed decision making.
During the 8th Workshop on Arguments Mining at EMNLP 2021, in an attempt to
suggest relevant solutions to this recent problem, the Quantitative Argument
Summarization - Key Point Analysis Shared Task was introduced. The task is divided
into Key Point Generation (KPG), which focuses on the identification and generation of
key statements from a text corpus, and Key Point Matching (KPM), that maps these
statements back to arguments of the original corpus. This subtask combination
contributes to a quantitative and explainable solution in the field of multi-document
argument summarization, which has been extensively studied in the English language,
however, the current landscape lacks research in a multilingual setting. This thesis
project is an attempt to adjust the task of Key Point Analysis (KPA) in Greek, a lowresource language. We propose baseline solutions for both subtasks by leveraging
available state-of-the-art Greek Language Models with a focus on the recently
introduced decoder-only Greek model, Meltemi, to explore both its NLU and NLG
capabilities. For both subtasks we use the official dataset of the KPA shared Task,
which we adjusted for the Greek language through machine and human translation. For
KPM a 4-bit quantized Meltemi-base model is finetuned for classification using PEFT
methods and compared to two encoder-only baselines. For KPG we experiment with
clustering based abstractive baselines in combination with encoder-decoder and
decoder-only models (foundation and instruction-tuned) in zero and few-shot inference
settings. The findings show the performance of Meltemi-base v1.0 in the KPM
classification task (avg mAP: 89.06) comparatively better than Greek encoder-only
based classifiers (avg mAP: 82.01) as well as that of Meltemi-Instruct v1.5 (R_1: 20.2,
R_2: 8.0, R_L: 19.1, BERTScore P: 74.0, R: 72.8, F1: 73.4 ) that outperforms Greek
T5 models (R_1: 12.3, R_2: 3.6, R_L: 11.0, BERTScore P: 66.0, R: 67.5, F1: 66.7 )
in KPG. The proposed approaches provide a promising methodology for extending the
KPA task in a multilingual setting.
Keywords:
multi-document, quantitative argument summarization, text classification, clustering methods, abstractive text generation
