Marina Litvak


2020

pdf bib
Automated Discovery of Mathematical Definitions in Text
Natalia Vanetik | Marina Litvak | Sergey Shevchuk | Lior Reznik
Proceedings of the 12th Language Resources and Evaluation Conference

Automatic definition extraction from texts is an important task that has numerous applications in several natural language processing fields such as summarization, analysis of scientific texts, automatic taxonomy generation, ontology generation, concept identification, and question answering. For definitions that are contained within a single sentence, this problem can be viewed as a binary classification of sentences into definitions and non-definitions. Definitions in scientific literature can be generic (Wikipedia) or more formal (mathematical articles). In this paper, we focus on automatic detection of one-sentence definitions in mathematical texts, which are difficult to separate from surrounding text. We experiment with several data representations, which include sentence syntactic structure and word embeddings, and apply deep learning methods such as convolutional neural network (CNN) and recurrent neural network (RNN), in order to identify mathematical definitions. Our experiments demonstrate the superiority of CNN and its combination with RNN, applied on the syntactically-enriched input representation. We also present a new dataset for definition extraction from mathematical texts. We demonstrate that the use of this dataset for training learning models improves the quality of definition extraction when these models are then used for other definition datasets. Our experiments with different domains approve that mathematical definitions require special treatment, and that using cross-domain learning is inefficient.

pdf bib
Detecting Troll Tweets in a Bilingual Corpus
Lin Miao | Mark Last | Marina Litvak
Proceedings of the 12th Language Resources and Evaluation Conference

During the past several years, a large amount of troll accounts has emerged with efforts to manipulate public opinion on social network sites. They are often involved in spreading misinformation, fake news, and propaganda with the intent of distracting and sowing discord. This paper aims to detect troll tweets in both English and Russian assuming that the tweets are generated by some “troll farm.” We reduce this task to the authorship verification problem of determining whether a single tweet is authored by a “troll farm” account or not. We evaluate a supervised classification approach with monolingual, cross-lingual, and bilingual training scenarios, using several machine learning algorithms, including deep learning. The best results are attained by the bilingual learning, showing the area under the ROC curve (AUC) of 0.875 and 0.828, for tweet classification in English and Russian test sets, respectively. It is noteworthy that these results are obtained using only raw text features, which do not require manual feature engineering efforts. In this paper, we introduce a resource of English and Russian troll tweets containing original tweets and translation from English to Russian, Russian to English. It is available for academic purposes.

pdf bib
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.

pdf bib
SCE-SUMMARY at the FNS 2020 shared task
Marina Litvak | Natalia Vanetik | Zvi Puchinsky
Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation

With the constantly growing amount of information, the need arises to automatically summarize this written information. One of the challenges in the summary is that it’s difficult to generalize. For example, summarizing a news article is very different from summarizing a financial earnings report. This paper reports an approach for summarizing financial texts, which are different from the documents from other domains at least in three parameters: length, structure, and format. Our approach considers these parameters, it is adapted to hierarchical structure of sections, document length, and special “language”. The approach builds an hierarchical summary, visualized as a tree with summaries under different discourse topics. The approach was evaluated using extrinsic and intrinsic automated evaluations, which are reported in this paper. As all participants of the Financial Narrative Summarisation (FNS 2020) shared task, we used FNS2020 dataset for evaluations.

pdf bib
Hierarchical summarization of financial reports with RUNNER
Marina Litvak | Natalia Vanetik | Zvi Puchinsky
Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation

With the constantly growing amount of information, the need arises to automatically summarize this written information. One of the challenges in the summary is that it’s difficult to generalize. For example, summarizing a news article is very different from summarizing a financial earnings report. This paper reports an approach for summarizing financial texts, which are different from the documents from other domains at least in three parameters: length, structure, and format. Our approach considers these parameters, it is adapted to hierarchical structure of sections, document length, and special “language”. The approach builds an hierarchical summary, visualized as a tree with summaries under different discourse topics. The approach was evaluated using extrinsic and intrinsic automated evaluations, which are reported in this paper. As all participants of the Financial Narrative Summarisation (FNS 2020) shared task, we used FNS2020 dataset for evaluations.

pdf bib
Twitter Data Augmentation for Monitoring Public Opinion on COVID-19 Intervention Measures
Lin Miao | Mark Last | Marina Litvak
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

The COVID-19 outbreak is an ongoing worldwide pandemic that was announced as a global health crisis in March 2020. Due to the enormous challenges and high stakes of this pandemic, governments have implemented a wide range of policies aimed at containing the spread of the virus and its negative effect on multiple aspects of our life. Public responses to various intervention measures imposed over time can be explored by analyzing the social media. Due to the shortage of available labeled data for this new and evolving domain, we apply data distillation methodology to labeled datasets from related tasks and a very small manually labeled dataset. Our experimental results show that data distillation outperforms other data augmentation methods on our task.

2019

pdf bib
In Conclusion Not Repetition: Comprehensive Abstractive Summarization with Diversified Attention Based on Determinantal Point Processes
Lei Li | Wei Liu | Marina Litvak | Natalia Vanetik | Zuying Huang
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Various Seq2Seq learning models designed for machine translation were applied for abstractive summarization task recently. Despite these models provide high ROUGE scores, they are limited to generate comprehensive summaries with a high level of abstraction due to its degenerated attention distribution. We introduce Diverse Convolutional Seq2Seq Model(DivCNN Seq2Seq) using Determinantal Point Processes methods(Micro DPPs and Macro DPPs) to produce attention distribution considering both quality and diversity. Without breaking the end to end architecture, DivCNN Seq2Seq achieves a higher level of comprehensiveness compared to vanilla models and strong baselines. All the reproducible codes and datasets are available online.

pdf bib
RANLP 2019 Multilingual Headline Generation Task Overview
Marina Litvak | John M. Conroy | Peter A. Rankel
Proceedings of the Workshop MultiLing 2019: Summarization Across Languages, Genres and Sources

The objective of the 2019 RANLP Multilingual Headline Generation (HG) Task is to explore some of the challenges highlighted by current state of the art approaches on creating informative headlines to news articles: non-descriptive headlines, out-of-domain training data, generating headlines from long documents which are not well represented by the head heuristic, and dealing with multilingual domain. This tasks makes available a large set of training data for headline generation and provides an evaluation methods for the task. Our data sets are drawn from Wikinews as well as Wikipedia. Participants were required to generate headlines for at least 3 languages, which were evaluated via automatic methods. A key aspect of the task is multilinguality. The task measures the performance of multilingual headline generation systems using the Wikipedia and Wikinews articles in multiple languages. The objective is to assess the performance of automatic headline generation techniques on text documents covering a diverse range of languages and topics outside the news domain.

pdf bib
HEvAS: Headline Evaluation and Analysis System
Marina Litvak | Natalia Vanetik | Itzhak Eretz Kdosha
Proceedings of the Workshop MultiLing 2019: Summarization Across Languages, Genres and Sources

Automatic headline generation is a subtask of one-line summarization with many reported applications. Evaluation of systems generating headlines is a very challenging and undeveloped area. We introduce the Headline Evaluation and Analysis System (HEvAS) that performs automatic evaluation of systems in terms of a quality of the generated headlines. HEvAS provides two types of metrics– one which measures the informativeness of a headline, and another that measures its readability. The results of evaluation can be compared to the results of baseline methods which are implemented in HEvAS. The system also performs the statistical analysis of the evaluation results and provides different visualization charts. This paper describes all evaluation metrics, baselines, analysis, and architecture, utilized by our system.

2017

pdf bib
Proceedings of the MultiLing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres
George Giannakopoulos | Elena Lloret | John M. Conroy | Josef Steinberger | Marina Litvak | Peter Rankel | Benoit Favre
Proceedings of the MultiLing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres

pdf bib
MultiLing 2017 Overview
George Giannakopoulos | John Conroy | Jeff Kubina | Peter A. Rankel | Elena Lloret | Josef Steinberger | Marina Litvak | Benoit Favre
Proceedings of the MultiLing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres

In this brief report we present an overview of the MultiLing 2017 effort and workshop, as implemented within EACL 2017. MultiLing is a community-driven initiative that pushes the state-of-the-art in Automatic Summarization by providing data sets and fostering further research and development of summarization systems. This year the scope of the workshop was widened, bringing together researchers that work on summarization across sources, languages and genres. We summarize the main tasks planned and implemented this year, the contributions received, and we also provide insights on next steps.

pdf bib
Query-based summarization using MDL principle
Marina Litvak | Natalia Vanetik
Proceedings of the MultiLing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres

Query-based text summarization is aimed at extracting essential information that answers the query from original text. The answer is presented in a minimal, often predefined, number of words. In this paper we introduce a new unsupervised approach for query-based extractive summarization, based on the minimum description length (MDL) principle that employs Krimp compression algorithm (Vreeken et al., 2011). The key idea of our approach is to select frequent word sets related to a given query that compress document sentences better and therefore describe the document better. A summary is extracted by selecting sentences that best cover query-related frequent word sets. The approach is evaluated based on the DUC 2005 and DUC 2006 datasets which are specifically designed for query-based summarization (DUC, 2005 2006). It competes with the best results.

2016

pdf bib
What’s up on Twitter? Catch up with TWIST!
Marina Litvak | Natalia Vanetik | Efi Levi | Michael Roistacher
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations

Event detection and analysis with respect to public opinions and sentiments in social media is a broad and well-addressed research topic. However, the characteristics and sheer volume of noisy Twitter messages make this a difficult task. This demonstration paper describes a TWItter event Summarizer and Trend detector (TWIST) system for event detection, visualization, textual description, and geo-sentiment analysis of real-life events reported in Twitter.

pdf bib
MUSEEC: A Multilingual Text Summarization Tool
Marina Litvak | Natalia Vanetik | Mark Last | Elena Churkin
Proceedings of ACL-2016 System Demonstrations

pdf bib
Social and linguistic behavior and its correlation to trait empathy
Marina Litvak | Jahna Otterbacher | Chee Siang Ang | David Atkins
Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)

A growing body of research exploits social media behaviors to gauge psychological character-istics, though trait empathy has received little attention. Because of its intimate link to the abil-ity to relate to others, our research aims to predict participants’ levels of empathy, given their textual and friending behaviors on Facebook. Using Poisson regression, we compared the vari-ance explained in Davis’ Interpersonal Reactivity Index (IRI) scores on four constructs (em-pathic concern, personal distress, fantasy, perspective taking), by two classes of variables: 1) post content and 2) linguistic style. Our study lays the groundwork for a greater understanding of empathy’s role in facilitating interactions on social media.

2015

pdf bib
Krimping texts for better summarization
Marina Litvak | Mark Last | Natalia Vanetik
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

pdf bib
Multilingual Summarization with Polytope Model
Natalia Vanetik | Marina Litvak
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

pdf bib
HEADS: Headline Generation as Sequence Prediction Using an Abstract Feature-Rich Space
Carlos A. Colmenares | Marina Litvak | Amin Mantrach | Fabrizio Silvestri
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2013

pdf bib
Mining the Gaps: Towards Polynomial Summarization
Marina Litvak | Natalia Vanetik
Proceedings of the Sixth International Joint Conference on Natural Language Processing

pdf bib
SmartNews: Towards content-sensitive ranking of comments
Marina Litvak | Leon Matz
The Companion Volume of the Proceedings of IJCNLP 2013: System Demonstrations

pdf bib
Multilingual Multi-Document Summarization with POLY2
Marina Litvak | Natalia Vanetik
Proceedings of the MultiLing 2013 Workshop on Multilingual Multi-document Summarization

pdf bib
Multilingual Single-Document Summarization with MUSE
Marina Litvak | Mark Last
Proceedings of the MultiLing 2013 Workshop on Multilingual Multi-document Summarization

2010

pdf bib
Towards multi-lingual summarization: A comparative analysis of sentence extraction methods on English and Hebrew corpora
Marina Litvak | Mark Last | Slava Kisilevich | Daniel Keim | Hagay Lipman | Assaf Ben Gur
Proceedings of the 4th Workshop on Cross Lingual Information Access

pdf bib
A New Approach to Improving Multilingual Summarization Using a Genetic Algorithm
Marina Litvak | Mark Last | Menahem Friedman
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

2008

pdf bib
Graph-Based Keyword Extraction for Single-Document Summarization
Marina Litvak | Mark Last
Coling 2008: Proceedings of the workshop Multi-source Multilingual Information Extraction and Summarization