Financial reports are essential for the decision-making processes of various stakeholders, containing vast amounts of both quantitative and qualitative data. As the business world becomes increasingly intricate, stakeholders require a swift means to understand a company’s financial status. Text summarization is a useful tool in this regard, aiming to present long texts concisely without losing their essence. Given the complexity and the structured nature of financial documents, summarizing them poses a significant challenge. This paper suggests a method employing Positional Language Models (PLMs), a subset of non-neural language models that assess the sequence of tokens in input data, for financial report summarization. Our proposed method is unsupervised, eliminating the need for training and ensuring computational efficiency for lengthy documents.

This paper presents the results and findings of the Financial Narrative Summarisation Shared Task on summarising UK, Greek, and Spanish annual reports. The shared task was organised as part of the 5th Financial Narrative Processing Workshop (FNP 2023). The Financial Narrative summarisation Shared Task (FNS 2023) has been running since 2020 as part of the Financial Narrative Processing (FNP) workshop series [15–20]. The shared task included one main challenge, which is the use of either abstractive or extractive automatic summarisers to summarise long documents in terms of UK, Greek, and Spanish financial annual reports. This shared task is the fourth to target financial documents. The data for the shared task was created and collected from publicly available annual reports published by firms listed on the Stock Exchanges of the UK, Greece, and Spain. A total number of 6 systems from 3 different teams participated in the shared task.

The first edition of the Implicit Author Characterization from Texts for Search and Retrieval (IACT'23) aims at bringing to the forefront the challenges involved in identifying and extracting from texts implicit information about authors (e.g., human or AI) and using it in IR tasks. The IACT workshop provides a common forum to consolidate multi-disciplinary efforts and foster discussions to identify the wide-ranging issues related to the task of extracting implicit author-related information from the textual content, including novel tasks and datasets. We will also discuss the ethical implications of implicit information extraction. In addition, we announce a shared task focused on automatically determining the literary epochs of written books.

Internet troll farms have recently been employed as a powerful and prevailing weapon of information warfare. Even though different tactics may be utilized by different groups of state-sponsored trolls, our goal is to leverage identified troll data for revealing new emerging trolls generating multilingual content. In this work, we adopt a model agnostic meta-learning framework making use of previously released troll farm datasets for the early detection of newly-emerged troll accounts from identified or unidentified troll farms. The detection earliness of various models is evaluated using variable amounts of the earliest tweets from the tested accounts. To evaluate the proposed meta-model, we compare it to several classification models based on different types of account features. Our experiments demonstrate the effectiveness of the meta-model requiring as few as ten tweets to detect a troll account with an average accuracy of 94%.

Automatic headline generation is a sub-task of oneline summarization with many reported applications. Evaluation of systems generating headlines is a very challenging and undeveloped area. In this paper, we introduce a system that performs automatic evaluation of systems in terms of a quality of the generated headlines. The evaluation is performed using multiple metrics for comparing evaluated headlines with the gold standard ones or measuring their coverage of main document topics. Both types of metrics evaluate headline's content and informativeness, but not grammatical structure. The only input required by our system is a set of documents with gold standard and automatically generated headlines. The Headline Evaluation System (HEvaS) provides a user with a choice from multiple (10) metrics, then calculates the chosen metrics, performs statistical analysis of the evaluated systems and visualizes the results. Multiple headline generation systems can be evaluated at the same run. This paper describes all evaluation metrics and architecture, utilized by our system. As an evaluation of the HEvaS, we perform a case study with a couple of baseline systems and report the results. Although we tested the system on English content only, the multilingual content can also be supported.

Automated text summarization extracts essential information from original text and presents it in a predefined number of words. In this paper, we introduce an unsupervised extractive summarization approach that takes its roots from the SLIM dataset compression algorithm [1] based on the Minimum Description Length (MDL) principle [2], [3]. Our approach represents text as a transactional dataset, where sentences are transactions and normalized words are items. We use the SLIM algorithm (SLIM is not an abbreviation, it is Dutch word for 'smart') to solve the main bottleneck of the MDL computation, which is the generation of all frequent itemsets as a first step of the model construction. Additionally, we add a diversity constraint to the model in order to decrease appearance of repeated information in a summary. We introduce DRIM (Diversed SLIM) algorithm that performs unsupervised summarization, both generic and query-based, and does not require parameter tuning. We evaluate our summarizer on texts in English, but it can be easily extended to other languages.