Automatic slides generation in the absence of training data

Disseminating the main research findings is one of the main requirements to become a successful researcher. Presentation slides are the most common way to present paper content. To support researchers in slide preparation, the NLP research community has explored the use of summarization techniques to automatically generate a draft of the slides consisting of the most salient sentences or phrases. State-of-the-art methods adopt a supervised approach, which first estimates global content relevance using a set of training papers and slides, then performs content selection by optimizing also section-level coverage. How- ever, in several domains and contexts there is a lack of training data, which hinders the use of supervised models. This paper focuses on addressing the above issue by applying unsupervised summarization methods. They are exploited to generate sentence-level summaries of the paper sections, which are then refined by applying an optimization step. Furthermore, it evaluates the quality of the output slides by taking into account the original paper structure as well. The results, achieved on a benchmark collection of papers and slides, show that unsupervised models performed better than supervised ones on specific paper facets, whereas they were competitive in terms of overall quality score

End-to-end Training For Financial Report Summarization

Quoted companies are requested to periodically publish financial reports in textual form. The annual financial reports typically include detailed financial and business information, thus giving relevant insights into company outlooks. However, a manual exploration of these financial reports could be very time consuming since most of the available information can be deemed as non-informative or redundant by expert readers. Hence, an increasing research interest has been devoted to automatically extracting domain-specific summaries, which include only the most relevant information. This paper describes the SumTO system architecture, which addresses the Shared Task of the Financial Narrative Summarisation (FNS) 2020 contest. The main task objective is to automatically extract the most informative, domain-specific textual content from financial, English-written documents. The aim is to create a summary of each company report covering all the business-relevant key points. To address the above-mentioned goal, we propose an end-to-end training method relying on Deep NLP techniques. The idea behind the system is to exploit the syntactic overlap between input sentences and ground-truth summaries to fine-tune pre-trained BERT embedding models, thus making such models tailored to the specific context. The achieved results confirm the effectiveness of the proposed method, especially when the goal is to select relatively long text snippets

Inferring multilingual domain-specific word embeddings from large document corpora

The use of distributed vector representations of words in Natural Language Processing has become established. To tailor general-purpose vector spaces to the context under analysis, several domain adaptation techniques have been proposed. They all require sufficiently large document corpora tailored to the target domains. However, in several cross-lingual NLP domains both large enough domain-specific document corpora and pre-trained domain-specific word vectors are hard to find for languages other than English. This paper aims at tackling the aforesaid issue. It proposes a new methodology to automatically infer aligned domain-specific word embeddings for a target language on the basis of the general-purpose and domain-specific models available for a source language (typically, English). The proposed inference method relies on a two-step process, which first automatically identifies domain-specific words and then opportunistically reuses the non-linear space transformations applied to the word vectors of the source language in order to learn how to tailor the vector space of the target language to the domain of interest. The performance of the proposed method was validated via extrinsic evaluation by addressing the established word retrieval task. To this aim, a new benchmark multilingual dataset, derived from Wikipedia, has been released. The results confirmed the effectiveness and usability of the proposed approach

Summarize Dates First: A Paradigm Shift in Timeline Summarization

Timeline summarization aims at presenting long news stories in a compact manner. State-of-the-art approaches first select the most relevant dates from the original event timeline then produce per-date news summaries. Date selection is driven by either per-date news content or date-level references. When coping with complex event data, characterized by inherent news flow redundancy, this pipeline may encounter relevant issues in both date selection and summarization due to a limited use of news content in date selection and no use of high-level temporal references (e.g., the past month). This paper proposes a paradigm shift in timeline summarization aimed at overcoming the above issues. It presents a new approach, namely Summarize Date First, which focuses on first generating date-level summaries then selecting the most relevant dates on top of summarized knowledge. In the latter stage, it performs date aggregations to consider high-level temporal references as well. The proposed pipeline also supports frequent incremental timeline updates more efficiently than previous approaches. We tested our unsupervised approach both on existing benchmark datasets and on a newly proposed benchmark dataset describing the COVID-19 news timeline. The achieved results were superior to state-of-the-art unsupervised methods and competitive against supervised ones

How Much Attention Should we Pay to Mosquitoes?

Mosquitoes are a major global health problem. They are responsible for the transmission of diseases and can have a large impact on local economies. Monitoring mosquitoes is therefore helpful in preventing the outbreak of mosquito-borne diseases. In this paper, we propose a novel data-driven approach that leverages Transformer-based models for the identification of mosquitoes in audio recordings. The task aims at detecting the time intervals corresponding to the acoustic mosquito events in an audio signal. We formulate the problem as a sequence tagging task and train a Transformer-based model using a real-world dataset collecting mosquito recordings. By leveraging the sequential nature of mosquito recordings, we formulate the training objective so that the input recordings do not require fine-grained annotations. We show that our approach is able to outperform baseline methods using standard evaluation metrics, albeit suffering from unexpectedly high false negatives detection rates. In view of the achieved results, we propose future directions for the design of more effective mosquito detection models

Cross-lingual timeline summarization

Timeline summarization methods analyze timestamped, topic-specific news article collections to select the key dates representing the event flow and to extract the most relevant per-date content. Existing approaches are all tailored to a single language. Hence, they are unable to combine topic-related content available in different languages. Enriching news timelines with multilingual content is particularly useful for (i) summarizing complex events, whose main facets are covered differently by media sources from different countries, and (ii) generating news timelines in low-resource languages, for which there is a lack of news content in the target language. This paper presents three alternative approaches to address cross-lingual timeline summarization. They combine state-of-the-art extractive summarization methods with machine translation steps at different stages of the timeline generation process. The paper also proposes novel Rouge-based evaluation metrics customized for cross-lingual timeline summarization with a twofold aim: (i) quantifying the ability of the cross-lingual process to enhance available content extraction in the target language and (ii) estimating summarizer effectiveness in conveying additional content from other languages. A new multilingual timeline benchmark dataset has been generated to allow a thorough analysis of the factors that mainly influence summarization performance

DANTE at GeoLingIt: Dialect-Aware Multi-Granularity Pre-training for Locating Tweets within Italy

This paper presents an NLP research system designed to geolocate tweets within Italy, a country renowned for its diverse linguistic landscape. Our methodology consists of a two-step process involving pre-training and fine-tuning phases. In the pre-training step, we take a semi-supervised approach and introduce two additional tasks. The primary objective of these tasks is to provide the language model with comprehensive knowledge of language varieties, focusing on both the sentence and token levels. Subsequently, during the fine-tuning phase, the model is adapted explicitly for two subtasks: coarse- and fine-grained variety geolocation. To evaluate the effectiveness of our methodology, we participate in the GeoLingIt 2023 shared task and assess our model’s performance using standard metrics. Ablation studies demonstrate the crucial role of thepre-training step in enhancing the model’s performance on both task

Transformer-based Non-Verbal Emotion Recognition: Exploring Model Portability across Speakers’ Genders

Recognizing emotions in non-verbal audio tracks requires a deep understanding of their underlying features. Traditional classifiers relying on excitation, prosodic, and vocal traction features are not always capable of effectively generalizing across speakers' genders. In the ComParE 2022 vocalisation sub-challenge we explore the use of a Transformer architecture trained on contrastive audio examples. We leverage augmented data to learn robust non-verbal emotion classifiers. We also investigate the impact of different audio transformations, including neural voice conversion, on the classifier capability to generalize across speakers' genders. The empirical findings indicate that neural voice conversion is beneficial in the pretraining phase, yielding an improved model generality, whereas is harmful at the finetuning stage as hinders model specialization for the task of non-verbal emotion recognition

E-MIMIC: Empowering Multilingual Inclusive Communication

Preserving diversity and inclusion is becoming a compelling need in both industry and academia. The ability to use appropriate forms of writing, speaking, and gestures is not widespread even in formal communications such as public calls, public announcements, official reports, and legal documents. The improper use of linguistic expressions can foment unacceptable forms of exclusion, stereotypes as well as forms of verbal violence against minorities, including women. Furthermore, existing machine translation tools are not designed to generate inclusive content. The present paper investigates a joint effort of the research communities of linguistics and Deep Learning Natural Language Understanding in fighting against non-inclusive, prejudiced language forms. It presents a methodology aimed at tackling the improper use of language in formal communication, with a particular attention paid to Romanic languages (Italian, in particular). State-of-the-art Deep Language Modeling architectures are exploited to automatically identify non-inclusive text snippets, suggest alternative forms, and produce inclusive text rephrasing. A preliminary evaluation conducted on a benchmark dataset shows promising results, i.e., 85% accuracy in predicting inclusive/non-inclusive communications

Transformer-based highlights extraction from scientific papers

Highlights are short sentences used to annotate scientific papers. They complement the abstract content by conveying the main result findings. To automate the process of paper annotation, highlights extraction aims at extracting from 3 to 5 paper sentences via supervised learning. Existing approaches rely on ad hoc linguistic features, which depend on the analyzed context, and apply recurrent neural networks, which are not effective in learning long-range text dependencies. This paper leverages the attention mechanism adopted in transformer models to improve the accuracy of sentence relevance estimation. Unlike existing approaches, it relies on the end-to-end training of a deep regression model. To attend patterns relevant to highlights content it also enriches sentence encodings with a section-level contextualization. The experimental results, achieved on three different benchmark datasets, show that the designed architecture is able to achieve significant performance improvements compared to the state-of-the-art