ymoslem/acl-abstracts huggingface dataset:
SPaR.txt, a Cheap Shallow Parsing Approach for Regulatory Texts (2021)

Automated Compliance Checking (ACC) systems aim to semantically parse building regulations to a set of rules. However, semantic parsing is known to be hard and requires large amounts of training data. The complexity of creating such training data has led to research that focuses on small sub-tasks, such as shallow parsing or the extraction of a limited subset of rules. This study introduces a shallow parsing task for which training data is relatively cheap to create, with the aim of learning a lexicon for ACC. We annotate a small domain-specific dataset of 200 sentences, SPaR.txt, and train a sequence tagger that achieves 79,93 F1-score on the test set. We then show through manual evaluation that the model identifies most (89,84{\%}) defined terms in a set of building regulation documents, and that both contiguous and discontiguous Multi-Word Expressions (MWE) are discovered with reasonable accuracy (70,3{\%}).
Capturing Logical Structure of Visually Structured Documents with Multimodal Transition Parser (2021)

While many NLP pipelines assume raw, clean texts, many texts we encounter in the wild, including a vast majority of legal documents, are not so clean, with many of them being visually structured documents (VSDs) such as PDFs. Conventional preprocessing tools for VSDs mainly focused on word segmentation and coarse layout analysis, whereas fine-grained logical structure analysis (such as identifying paragraph boundaries and their hierarchies) of VSDs is underexplored. To that end, we proposed to formulate the task as prediction of {\textquotedblleft}transition labels{\textquotedblright} between text fragments that maps the fragments to a tree, and developed a feature-based machine learning system that fuses visual, textual and semantic cues. Our system is easily customizable to different types of VSDs and it significantly outperformed baselines in identifying different structures in VSDs. For example, our system obtained a paragraph boundary detection F1 score of 0.953 which is significantly better than a popular PDF-to-text tool with an F1 score of 0.739.
Legal Terminology Extraction with the Termolator (2021)

Domain-specific terminology is ubiquitous in legal documents. Despite potential utility in populating glossaries and ontologies or as arguments in information extraction and document classification tasks, there has been limited work done for legal terminology extraction. This paper describes some work to remedy this omission. In the described research, we make some modifications to the Termolator, a high-performing, open-source terminology extractor which has been tuned to scientific articles. Our changes are designed to improve the Termolator`s results when applied to United States Supreme Court decisions. Unaltered and using the recommended settings, the original Termolator provides a list of terminology with a precision of 23{\%} and 25{\%} for the categories of economic activity (development set) and criminal procedures (test set) respectively. These were the most frequently occurring broad issues in Washington University in St. Louis Database corpus, a database of Supreme Court decisions that have been manually classified by topic. Our contribution includes the introduction of several legal domain-specific filtration steps and changes to the web search relevance score; each incrementally improved precision culminating in a combined precision of 63{\%} and 65{\%}. We also evaluated the baseline version of the Termolator on more specific subcategories and on broad issues with fewer cases. Our results show that a narrowed scope as well as smaller document numbers significantly lower the precision. In both cases, the modifications to the Termolator improve precision.
Supervised Identification of Participant Slots in Contracts (2021)

This paper presents a technique for the identification of participant slots in English language contracts. Taking inspiration from unsupervised slot extraction techniques, the system presented here uses a supervised approach to identify terms used to refer to a genre-specific slot in novel contracts. We evaluate the system in multiple feature configurations to demonstrate that the best performing system in both genres of contracts omits the exact mention form from consideration{---}even though such mention forms are often the name of the slot under consideration{---}and is instead based solely on the dependency label and parent; in other words, a more reliable quantification of a party`s role in a contract is found in what they do rather than what they are named.
Named Entity Recognition in Historic Legal Text: A Transformer and State Machine Ensemble Method (2021)

Older legal texts are often scanned and digitized via Optical Character Recognition (OCR), which results in numerous errors. Although spelling and grammar checkers can correct much of the scanned text automatically, Named Entity Recognition (NER) is challenging, making correction of names difficult. To solve this, we developed an ensemble language model using a transformer neural network architecture combined with a finite state machine to extract names from English-language legal text. We use the US-based English language Harvard Caselaw Access Project for training and testing. Then, the extracted names are subjected to heuristic textual analysis to identify errors, make corrections, and quantify the extent of problems. With this system, we are able to extract most names, automatically correct numerous errors and identify potential mistakes that can later be reviewed for manual correction.
Summarization of German Court Rulings (2021)

Historically speaking, the German legal language is widely neglected in NLP research, especially in summarization systems, as most of them are based on English newspaper articles. In this paper, we propose the task of automatic summarization of German court rulings. Due to their complexity and length, it is of critical importance that legal practitioners can quickly identify the content of a verdict and thus be able to decide on the relevance for a given legal case. To tackle this problem, we introduce a new dataset consisting of 100k German judgments with short summaries. Our dataset has the highest compression ratio among the most common summarization datasets. German court rulings contain much structural information, so we create a pre-processing pipeline tailored explicitly to the German legal domain. Additionally, we implement multiple extractive as well as abstractive summarization systems and build a wide variety of baseline models. Our best model achieves a ROUGE-1 score of 30.50. Therefore with this work, we are laying the crucial groundwork for further research on German summarization systems.
Learning from Limited Labels for Long Legal Dialogue (2021)

We study attempting to achieve high accuracy information extraction of case factors from a challenging dataset of parole hearings, which, compared to other legal NLP datasets, has longer texts, with fewer labels. On this corpus, existing work directly applying pretrained neural models has failed to extract all but a few relatively basic items with little improvement over rule-based extraction. We address two challenges posed by existing work: training on long documents and reasoning over complex speech patterns. We use a similar approach to the two-step open-domain question answering approach by using a Reducer to extract relevant text segments and a Producer to generate both extractive answers and non-extractive classifications. In a context like ours, with limited labeled data, we show that a superior approach for strong performance within limited development time is to use a combination of a rule-based Reducer and a neural Producer. We study four representative tasks from the parole dataset. On all four, we improve extraction from the previous benchmark of 0.41{--}0.63 to 0.83{--}0.89 F1.
Automating Claim Construction in Patent Applications: The CMUmine Dataset (2021)

Intellectual Property (IP) in the form of issued patents is a critical and very desirable element of innovation in high-tech. In this position paper, we explore the possibility of automating the legal task of Claim Construction in patent applications via Natural Language Processing (NLP) and Machine Learning (ML). To this end, we first create a large dataset known as CMUmine{\texttrademark}and then demonstrate that, using NLP and ML techniques the Claim Construction in patent applications, a crucial legal task currently performed by IP attorneys, can be automated. To the best of our knowledge, this is the first public patent application dataset. Our results look very promising in automating the patent application process.
Effectively Leveraging BERT for Legal Document Classification (2021)

Bidirectional Encoder Representations from Transformers (BERT) has achieved state-of-the-art performances on several text classification tasks, such as GLUE and sentiment analysis. Recent work in the legal domain started to use BERT on tasks, such as legal judgement prediction and violation prediction. A common practise in using BERT is to fine-tune a pre-trained model on a target task and truncate the input texts to the size of the BERT input (e.g. at most 512 tokens). However, due to the unique characteristics of legal documents, it is not clear how to effectively adapt BERT in the legal domain. In this work, we investigate how to deal with long documents, and how is the importance of pre-training on documents from the same domain as the target task. We conduct experiments on the two recent datasets: ECHR Violation Dataset and the Overruling Task Dataset, which are multi-label and binary classification tasks, respectively. Importantly, on average the number of tokens in a document from the ECHR Violation Dataset is more than 1,600. While the documents in the Overruling Task Dataset are shorter (the maximum number of tokens is 204). We thoroughly compare several techniques for adapting BERT on long documents and compare different models pre-trained on the legal and other domains. Our experimental results show that we need to explicitly adapt BERT to handle long documents, as the truncation leads to less effective performance. We also found that pre-training on the documents that are similar to the target task would result in more effective performance on several scenario.
Semi-automatic Triage of Requests for Free Legal Assistance (2021)

Free legal assistance is critically under-resourced, and many of those who seek legal help have their needs unmet. A major bottleneck in the provision of free legal assistance to those most in need is the determination of the precise nature of the legal problem. This paper describes a collaboration with a major provider of free legal assistance, and the deployment of natural language processing models to assign area-of-law categories to real-world requests for legal assistance. In particular, we focus on an investigation of models to generate efficiencies in the triage process, but also the risks associated with naive use of model predictions, including fairness across different user demographics.
Automatic Resolution of Domain Name Disputes (2021)

We introduce the new task of domain name dispute resolution (DNDR), that predicts the outcome of a process for resolving disputes about legal entitlement to a domain name. TheICANN UDRP establishes a mandatory arbitration process for a dispute between a trade-mark owner and a domain name registrant pertaining to a generic Top-Level Domain (gTLD) name (one ending in .COM, .ORG, .NET, etc). The nature of the problem leads to a very skewed data set, which stems from being able to register a domain name with extreme ease, very little expense, and no need to prove an entitlement to it. In this paper, we describe thetask and associated data set. We also present benchmarking results based on a range of mod-els, which show that simple baselines are in general difficult to beat due to the skewed data distribution, but in the specific case of the respondent having submitted a response, a fine-tuned BERT model offers considerable improvements over a majority-class model
Sentence-level Planning for Especially Abstractive Summarization (2021)

Abstractive summarization models heavily rely on copy mechanisms, such as the pointer network or attention, to achieve good performance, measured by textual overlap with reference summaries. As a result, the generated summaries stay close to the formulations in the source document. We propose the *sentence planner* model to generate more abstractive summaries. It includes a hierarchical decoder that first generates a representation for the next summary sentence, and then conditions the word generator on this representation. Our generated summaries are more abstractive and at the same time achieve high ROUGE scores when compared to human reference summaries. We verify the effectiveness of our design decisions with extensive evaluations.
Template-aware Attention Model for Earnings Call Report Generation (2021)

Earning calls are among important resources for investors and analysts for updating their price targets. Firms usually publish corresponding transcripts soon after earnings events. However, raw transcripts are often too long and miss the coherent structure. To enhance the clarity, analysts write well-structured reports for some important earnings call events by analyzing them, requiring time and effort. In this paper, we propose TATSum (Template-Aware aTtention model for Summarization), a generalized neural summarization approach for structured report generation, and evaluate its performance in the earnings call domain. We build a large corpus with thousands of transcripts and reports using historical earnings events. We first generate a candidate set of reports from the corpus as potential soft templates which do not impose actual rules on the output. Then, we employ an encoder model with margin-ranking loss to rank the candidate set and select the best quality template. Finally, the transcript and the selected soft template are used as input in a seq2seq framework for report generation. Empirical results on the earnings call dataset show that our model significantly outperforms state-of-the-art models in terms of informativeness and structure.
Knowledge and Keywords Augmented Abstractive Sentence Summarization (2021)

In this paper, we study the abstractive sentence summarization. There are two essential information features that can influence the quality of news summarization, which are topic keywords and the knowledge structure of the news text. Besides, the existing knowledge encoder has poor performance on sparse sentence knowledge structure. Considering these, we propose KAS, a novel Knowledge and Keywords Augmented Abstractive Sentence Summarization framework. Tri-encoders are utilized to integrate contexts of original text, knowledge structure and keywords topic simultaneously, with a special linearized knowledge structure. Automatic and human evaluations demonstrate that KAS achieves the best performances.
Rewards with Negative Examples for Reinforced Topic-Focused Abstractive Summarization (2021)

We consider the problem of topic-focused abstractive summarization, where the goal is to generate an abstractive summary focused on a particular topic, a phrase of one or multiple words. We hypothesize that the task of generating topic-focused summaries can be improved by showing the model what it must not focus on. We introduce a deep reinforcement learning approach to topic-focused abstractive summarization, trained on rewards with a novel negative example baseline. We define the input in this problem as the source text preceded by the topic. We adapt the CNN-Daily Mail and New York Times summarization datasets for this task. We then show through experiments on existing rewards that the use of a negative example baseline can outperform the use of a self-critical baseline, in Rouge, BERTScore, and human evaluation metrics.
A Novel Wikipedia based Dataset for Monolingual and Cross-Lingual Summarization (2021)

Cross-lingual summarization is a challenging task for which there are no cross-lingual scientific resources currently available. To overcome the lack of a high-quality resource, we present a new dataset for monolingual and cross-lingual summarization considering the English-German pair. We collect high-quality, real-world cross-lingual data from Spektrum der Wissenschaft, which publishes human-written German scientific summaries of English science articles on various subjects. The generated Spektrum dataset is small; therefore, we harvest a similar dataset from the Wikipedia Science Portal to complement it. The Wikipedia dataset consists of English and German articles, which can be used for monolingual and cross-lingual summarization. Furthermore, we present a quantitative analysis of the datasets and results of empirical experiments with several existing extractive and abstractive summarization models. The results suggest the viability and usefulness of the proposed dataset for monolingual and cross-lingual summarization.
Evaluation of Summarization Systems across Gender, Age, and Race (2021)

Summarization systems are ultimately evaluated by human annotators and raters. Usually, annotators and raters do not reflect the demographics of end users, but are recruited through student populations or crowdsourcing platforms with skewed demographics. For two different evaluation scenarios {--} evaluation against gold summaries and system output ratings {--} we show that summary evaluation is sensitive to protected attributes. This can severely bias system development and evaluation, leading us to build models that cater for some groups rather than others.
Evaluation of Abstractive Summarisation Models with Machine Translation in Deliberative Processes (2021)

We present work on summarising deliberative processes for non-English languages. Unlike commonly studied datasets, such as news articles, this deliberation dataset reflects difficulties of combining multiple narratives, mostly of poor grammatical quality, in a single text. We report an extensive evaluation of a wide range of abstractive summarisation models in combination with an off-the-shelf machine translation model. Texts are translated into English, summarised, and translated back to the original language. We obtain promising results regarding the fluency, consistency and relevance of the summaries produced. Our approach is easy to implement for many languages for production purposes by simply changing the translation model.
Capturing Speaker Incorrectness: Speaker-Focused Post-Correction for Abstractive Dialogue Summarization (2021)

In this paper, we focus on improving the quality of the summary generated by neural abstractive dialogue summarization systems. Even though pre-trained language models generate well-constructed and promising results, it is still challenging to summarize the conversation of multiple participants since the summary should include a description of the overall situation and the actions of each speaker. This paper proposes self-supervised strategies for speaker-focused post-correction in abstractive dialogue summarization. Specifically, our model first discriminates which type of speaker correction is required in a draft summary and then generates a revised summary according to the required type. Experimental results show that our proposed method adequately corrects the draft summaries, and the revised summaries are significantly improved in both quantitative and qualitative evaluations.
Measuring Similarity of Opinion-bearing Sentences (2021)

For many NLP applications of online reviews, comparison of two opinion-bearing sentences is key. We argue that, while general purpose text similarity metrics have been applied for this purpose, there has been limited exploration of their applicability to opinion texts. We address this gap in the literature, studying: (1) how humans judge the similarity of pairs of opinion-bearing sentences; and, (2) the degree to which existing text similarity metrics, particularly embedding-based ones, correspond to human judgments. We crowdsourced annotations for opinion sentence pairs and our main findings are: (1) annotators tend to agree on whether or not opinion sentences are similar or different; and (2) embedding-based metrics capture human judgments of {\textquotedblleft}opinion similarity{\textquotedblright} but not {\textquotedblleft}opinion difference{\textquotedblright}. Based on our analysis, we identify areas where the current metrics should be improved. We further propose to learn a similarity metric for opinion similarity via fine-tuning the Sentence-BERT sentence-embedding network based on review text and weak supervision by review ratings. Experiments show that our learned metric outperforms existing text similarity metrics and especially show significantly higher correlations with human annotations for differing opinions.
EASE: Extractive-Abstractive Summarization End-to-End using the Information Bottleneck Principle (2021)

Current abstractive summarization systems outperform their extractive counterparts, but their widespread adoption is inhibited by the inherent lack of interpretability. Extractive summarization systems, though interpretable, suffer from redundancy and possible lack of coherence. To achieve the best of both worlds, we propose EASE, an extractive-abstractive framework that generates concise abstractive summaries that can be traced back to an extractive summary. Our framework can be applied to any evidence-based text generation problem and can accommodate various pretrained models in its simple architecture. We use the Information Bottleneck principle to jointly train the extraction and abstraction in an end-to-end fashion. Inspired by previous research that humans use a two-stage framework to summarize long documents (Jing and McKeown, 2000), our framework first extracts a pre-defined amount of evidence spans and then generates a summary using only the evidence. Using automatic and human evaluations, we show that the generated summaries are better than strong extractive and extractive-abstractive baselines.
Context or No Context? A preliminary exploration of human-in-the-loop approach for Incremental Temporal Summarization in meetings (2021)

Incremental meeting temporal summarization, summarizing relevant information of partial multi-party meeting dialogue, is emerging as the next challenge in summarization research. Here we examine the extent to which human abstractive summaries of the preceding increments (context) can be combined with extractive meeting dialogue to generate abstractive summaries. We find that previous context improves ROUGE scores. Our findings further suggest that contexts begin to outweigh the dialogue. Using keyphrase extraction and semantic role labeling (SRL), we find that SRL captures relevant information without overwhelming the the model architecture. By compressing the previous contexts by {\textasciitilde}70{\%}, we achieve better ROUGE scores over our baseline models. Collectively, these results suggest that context matters, as does the way in which context is presented to the model.
Are We Summarizing the Right Way? A Survey of Dialogue Summarization Data Sets (2021)

Dialogue summarization is a long-standing task in the field of NLP, and several data sets with dialogues and associated human-written summaries of different styles exist. However, it is unclear for which type of dialogue which type of summary is most appropriate. For this reason, we apply a linguistic model of dialogue types to derive matching summary items and NLP tasks. This allows us to map existing dialogue summarization data sets into this model and identify gaps and potential directions for future work. As part of this process, we also provide an extensive overview of existing dialogue summarization data sets.