GenAI4ELab/papercli-papers huggingface dataset:
Zero-Shot Cross-Lingual Summarization via Large Language Models
Authors: Jiaan Wang; Yunlong Liang; Fandong Meng; Beiqi Zou; Zhixu Li; Jianfeng Qu; Jie Zhou
Venue: EMNLP 2023 (Proceedings of the 4th New Frontiers in Summarization Workshop)

Abstract: Given a document in a source language, cross-lingual summarization (CLS) aims to generate a summary in a different target language. Recently, the emergence of Large Language Models (LLMs), such as GPT-3.5, ChatGPT and GPT-4, has attracted wide attention from the computational linguistics community. However, it is not yet known the performance of LLMs on CLS. In this report, we empirically use various prompts to guide LLMs to perform zero-shot CLS from different paradigms (i.e., end-to-end and pipeline), and provide a preliminary evaluation on the generated summaries. We find that ChatGPT and GPT-4 originally prefer to produce lengthy summaries with detailed information. These two LLMs can further balance informativeness and conciseness with the help of an interactive prompt, significantly improving their CLS performance. Experimental results on three widely-used CLS datasets show that GPT-4 achieves state-of-the-art zero-shot CLS performance, and performs competitively compared with the fine-tuned mBART-50. Moreover, we also find some multi-lingual and bilingual LLMs (i.e., BLOOMZ, ChatGLM-6B, Vicuna-13B and ChatYuan) have limited zero-shot CLS ability. Due to the composite nature of CLS, which requires models to perform summarization and translation simultaneously, accomplishing this task in a zero-shot manner is even a challenge for LLMs. Therefore, we sincerely hope and recommend future LLM research could use CLS as a testbed.
SimCSum: Joint Learning of Simplification and Cross-lingual Summarization for Cross-lingual Science Journalism
Authors: Mehwish Fatima; Tim Kolber; Katja Markert; Michael Strube
Venue: EMNLP 2023 (Proceedings of the 4th New Frontiers in Summarization Workshop)

Abstract: Cross-lingual science journalism is a recently introduced task that generates popular science summaries of scientific articles different from the source language for non-expert readers. A popular science summary must contain salient content of the input document while focusing on coherence and comprehensibility. Meanwhile, generating a cross-lingual summary from the scientific texts in a local language for the targeted audience is challenging. Existing research on cross-lingual science journalism investigates the task with a pipeline model to combine text simplification and cross-lingual summarization. We extend the research in cross-lingual science journalism by introducing a novel, multi-task learning architecture that combines the aforementioned NLP tasks. Our approach is to jointly train the two high-level NLP tasks in SimCSum for generating cross-lingual popular science summaries. We investigate the performance of SimCSum against the pipeline model and several other strong baselines with several evaluation metrics and human evaluation. Overall, SimCSum demonstrates statistically significant improvements over the state-of-the-art on two non-synthetic cross-lingual scientific datasets. Furthermore, we conduct an in-depth investigation into the linguistic properties of generated summaries and an error analysis.
Extract, Select and Rewrite: A Modular Sentence Summarization Method
Authors: Shuo Guan; Vishakh Padmakumar
Venue: EMNLP 2023 (Proceedings of the 4th New Frontiers in Summarization Workshop)

Abstract: A modular approach has the advantage of being compositional and controllable, comparing to most end-to-end models. In this paper we propose Extract-Select-Rewrite (ESR), a three-phase abstractive sentence summarization method. We decompose summarization into three stages: (i) knowledge extraction, where we extract relation triples from the text using off-the-shelf tools; (ii) content selection, where a subset of triples are selected; and (iii) rewriting, where the selected triple are realized into natural language. Our results demonstrates that ESR is competitive with the best end-to-end models while being more faithful. %than these baseline models. Being modular, ESR’s modules can be trained on separate data which is beneficial in low-resource settings and enhancing the style controllability on text generation.
Summarization-based Data Augmentation for Document Classification
Authors: Yueguan Wang; Naoki Yoshinaga
Venue: EMNLP 2023 (Proceedings of the 4th New Frontiers in Summarization Workshop)

Abstract: Despite the prevalence of pretrained language models in natural language understanding tasks, understanding lengthy text such as document is still challenging due to the data sparseness problem. Inspired by that humans develop their ability of understanding lengthy text form reading shorter text, we propose a simple yet effective summarization-based data augmentation, SUMMaug, for document classification. We first obtain easy-to-learn examples for the target document classification task by summarizing the input of the original training examples, while optionally merging the original labels to conform to the summarized input. We then use the generated pseudo examples to perform curriculum learning. Experimental results on two datasets confirmed the advantage of our method compared to existing baseline methods in terms of robustness and accuracy. We release our code and data at https://github.com/etsurin/summaug.
In-context Learning of Large Language Models for Controlled Dialogue Summarization: A Holistic Benchmark and Empirical Analysis
Authors: Yuting Tang; Ratish Puduppully; Zhengyuan Liu; Nancy Chen
Venue: EMNLP 2023 (Proceedings of the 4th New Frontiers in Summarization Workshop)

Abstract: Large Language Models (LLMs) have shown significant performance in numerous NLP tasks, including summarization and controlled text generation. A notable capability of LLMs is in-context learning (ICL), where the model learns new tasks using input-output pairs in the prompt without any parameter update. However, the performance of LLMs in the context of few-shot abstractive dialogue summarization remains underexplored. This study evaluates various state-of-the-art LLMs on the SAMSum dataset within a few-shot framework. We assess these models in both controlled (entity control, length control, and person-focused planning) and uncontrolled settings, establishing a comprehensive benchmark in few-shot dialogue summarization. Our findings provide insights into summary quality and model controllability, offering a crucial reference for future research in dialogue summarization.
From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting
Authors: Griffin Adams; Alex Fabbri; Faisal Ladhak; Eric Lehman; Noémie Elhadad
Venue: EMNLP 2023 (Proceedings of the 4th New Frontiers in Summarization Workshop)

Abstract: Selecting the “right” amount of information to include in a summary is a difficult task. A good summary should be detailed and entity-centric without being overly dense and hard to follow. To better understand this tradeoff, we solicit increasingly dense GPT-4 summaries with what we refer to as a “Chain of Density” (CoD) prompt. Specifically, GPT-4 generates an initial entity-sparse summary before iteratively incorporating missing salient entities without increasing the length. Summaries generated by CoD are more abstractive, exhibit more fusion, and have less of a lead bias than GPT-4 summaries generated by a vanilla prompt. We conduct a human preference study on 100 CNN DailyMail articles and find that humans prefer GPT-4 summaries that are more dense than those generated by a vanilla prompt and almost as dense as human written summaries. Qualitative analysis supports the notion that there exists a tradeoff between informativeness and readability. 500 annotated CoD summaries, as well as an extra 5,000 unannotated summaries, are freely available on HuggingFace (https://huggingface.co/datasets/griffin/chain_of_density).
Generating Extractive and Abstractive Summaries in Parallel from Scientific Articles Incorporating Citing Statements
Authors: Sudipta Singha Roy; Robert E. Mercer
Venue: EMNLP 2023 (Proceedings of the 4th New Frontiers in Summarization Workshop)

Abstract: Summarization of scientific articles often overlooks insights from citing papers, focusing solely on the document’s content. To incorporate citation contexts, we develop a model to summarize a scientific document using the information in the source and citing documents. It concurrently generates abstractive and extractive summaries, each enhancing the other. The extractive summarizer utilizes a blend of heterogeneous graph-based neural networks and graph attention networks, while the abstractive summarizer employs an autoregressive decoder. These modules exchange control signals through the loss function, ensuring the creation of high-quality summaries in both styles.
Supervising the Centroid Baseline for Extractive Multi-Document Summarization
Authors: Simão Gonçalves; Gonçalo Correia; Diogo Pernes; Afonso Mendes
Venue: EMNLP 2023 (Proceedings of the 4th New Frontiers in Summarization Workshop)

Abstract: The centroid method is a simple approach for extractive multi-document summarization and many improvements to its pipeline have been proposed. We further refine it by adding a beam search process to the sentence selection and also a centroid estimation attention model that leads to improved results. We demonstrate this in several multi-document summarization datasets, including in a multilingual scenario.
DebateKG – Automatic Policy Debate Case Creation with Semantic Knowledge Graphs
Authors: Allen Roush; David Mezzetti
Venue: EMNLP 2023 (Proceedings of the 4th New Frontiers in Summarization Workshop)

Abstract: Recent work within the Argument Mining community has shown the applicability of Natural Language Processing systems for solving problems found within competitive debate. One of the most important tasks within competitive debate is for debaters to create high quality debate cases. We show that effective debate cases can be constructed using constrained shortest path traversals on Argumentative Semantic Knowledge Graphs. We study this potential in the context of a type of American Competitive Debate, called “Policy Debate”, which already has a large scale dataset targeting it called “DebateSum”. We significantly improve upon DebateSum by introducing 53180 new examples, as well as further useful metadata for every example, to the dataset. We leverage the txtai semantic search and knowledge graph toolchain to produce and contribute 9 semantic knowledge graphs built on this dataset. We create a unique method for evaluating which knowledge graphs are better in the context of producing policy debate cases. A demo which automatically generates debate cases, along with all other code and the Knowledge Graphs, are open-sourced and made available to the public here: https://huggingface.co/spaces/Hellisotherpeople/DebateKG
Analyzing Multi-Sentence Aggregation in Abstractive Summarization via the Shapley Value
Authors: Jingyi He; Meng Cao; Jackie Chi Kit Cheung
Venue: EMNLP 2023 (Proceedings of the 4th New Frontiers in Summarization Workshop)

Abstract: Abstractive summarization systems aim to write concise summaries capturing the most essential information of the input document in their own words. One of the ways to achieve this is to gather and combine multiple pieces of information from the source document, a process we call aggregation. Despite its importance, the extent to which both reference summaries in benchmark datasets and system-generated summaries require aggregation is yet unknown. In this work, we propose AggSHAP, a measure of the degree of aggregation in a summary sentence. We show that AggSHAP distinguishes multi-sentence aggregation from single-sentence extraction or paraphrasing through automatic and human evaluations. We find that few reference or model-generated summary sentences have a high degree of aggregation measured by the proposed metric. We also demonstrate negative correlations between AggSHAP and other quality scores of system summaries. These findings suggest the need to develop new tasks and datasets to encourage multi-sentence aggregation in summarization.
Improving Multi-Stage Long Document Summarization with Enhanced Coarse Summarizer
Authors: Jinhyeong Lim; Hyun-Je Song
Venue: EMNLP 2023 (Proceedings of the 4th New Frontiers in Summarization Workshop)

Abstract: Multi-stage long document summarization, which splits a long document as multiple segments and each of which is used to generate a coarse summary in multiple stage, and then the final summary is produced using the last coarse summary, is a flexible approach to capture salient information from the long document. Even if the coarse summary affects the final summary, however, the coarse summarizer in the existing multi-stage summarization is coarsely trained using data segments that are not useful to generate the final summary. In this paper, we propose a novel method for multi-stage long document summarization. The proposed method first generates new segment pairs, ensuring that all of them are relevant to generating the final summary. We then incorporate contrastive learning into the training of the coarse summarizer, which tries to maximize the similarities between source segments and the target summary during training. Through extensive experiments on six long document summarization datasets, we demonstrate that our proposed method not only enhances the existing multi-stage long document summarization approach, but also achieves performance comparable to state-of-the-art methods, including those utilizing large language models for long document summarization.
Anthropomorphization of AI: Opportunities and Risks
Authors: Ameet Deshpande; Tanmay Rajpurohit; Karthik Narasimhan; Ashwin Kalyan
Venue: EMNLP 2023 (Proceedings of the Natural Legal Language Processing Workshop 2023)

Abstract: Anthropomorphization is the tendency to attribute human-like traits to non-human entities. It is prevalent in many social contexts – children anthropomorphize toys, adults do so with brands, and it is a literary device. It is also a versatile tool in science, with behavioral psychology and evolutionary biology meticulously documenting its consequences. With widespread adoption of AI systems, and the push from stakeholders to make it human-like through alignment techniques, human voice, and pictorial avatars, the tendency for users to anthropomorphize it increases significantly. We take a dyadic approach to understanding this phenomenon with large language models (LLMs) by studying (1) the objective legal implications, as analyzed through the lens of the recent blueprint of AI bill of rights and the (2) subtle psychological aspects customization and anthropomorphization. We find that anthropomorphized LLMs customized for different user bases violate multiple provisions in the legislative blueprint. In addition, we point out that anthropomorphization of LLMs affects the influence they can have on their users, thus having the potential to fundamentally change the nature of human-AI interaction, with potential for manipulation and negative influence. With LLMs being hyper-personalized for vulnerable groups like children and patients among others, our work is a timely and important contribution. We propose a conservative strategy for the cautious use of anthropomorphization to improve trustworthiness of AI systems.
NOMOS: Navigating Obligation Mining in Official Statutes
Authors: Andrea Pennisi; Elvira González Hernández; Nina Koivula
Venue: EMNLP 2023 (Proceedings of the Natural Legal Language Processing Workshop 2023)

Abstract: The process of identifying obligations in a legal text is not a straightforward task, because not only are the documents long, but the sentences therein are long as well. As a result of long elements in the text, law is more difficult to interpret (Coupette et al., 2021). Moreover, the identification of obligations relies not only on the clarity and precision of the language used but also on the unique perspectives, experiences, and knowledge of the reader. In particular, this paper addresses the problem of identifyingobligations using machine and deep learning approaches showing a full comparison between both methodologies and proposing a new approach called NOMOS based on the combination of Positional Embeddings (PE) and Temporal Convolutional Networks (TCNs). Quantitative and qualitative experiments, conducted on legal regulations 1, demonstrate the effectiveness of the proposed approach.
Long Text Classification using Transformers with Paragraph Selection Strategies
Authors: Mohit Tuteja; Daniel González Juclà
Venue: EMNLP 2023 (Proceedings of the Natural Legal Language Processing Workshop 2023)

Abstract: In the legal domain, we often perform classification tasks on very long documents, for example court judgements. These documents often contain thousands of words, so the length of these documents poses a challenge for this modelling task. In this research paper, we present a comprehensive evaluation of various strategies to perform long text classification using Transformers in conjunction with strategies to select document chunks using traditional NLP models. We conduct our experiments on 6 benchmark datasets comprising lengthy documents, 4 of which are publicly available. Each dataset has a median word count exceeding 1,000. Our evaluation encompasses state-of-the-art Transformer models, such as RoBERTa, Longformer, HAT, MEGA and LegalBERT and compares them with a traditional baseline TF-IDF + Neural Network (NN) model. We investigate the effectiveness of pre-training on large corpora, fine tuning strategies, and transfer learning techniques in the context of long text classification.
Do Language Models Learn about Legal Entity Types during Pretraining?
Authors: Claire Barale; Michael Rovatsos; Nehal Bhuta
Venue: EMNLP 2023 (Proceedings of the Natural Legal Language Processing Workshop 2023)

Abstract: Language Models (LMs) have proven their ability to acquire diverse linguistic knowledge during the pretraining phase, potentially serving as a valuable source of incidental supervision for downstream tasks. However, there has been limited research conducted on the retrieval of domain-specific knowledge, and specifically legal knowledge. We propose to explore the task of Entity Typing, serving as a proxy for evaluating legal knowledge as an essential aspect of text comprehension, and a foundational task to numerous downstream legal NLP applications. Through systematic evaluation and analysis and two types of prompting (cloze sentences and QA-based templates) and to clarify the nature of these acquired cues, we compare diverse types and lengths of entities both general and domain-specific entities, semantics or syntax signals, and different LM pretraining corpus (generic and legal-oriented) and architectures (encoder BERT-based and decoder-only with Llama2). We show that (1) Llama2 performs well on certain entities and exhibits potential for substantial improvement with optimized prompt templates, (2) law-oriented LMs show inconsistent performance, possibly due to variations in their training corpus, (3) LMs demonstrate the ability to type entities even in the case of multi-token entities, (4) all models struggle with entities belonging to sub-domains of the law (5) Llama2 appears to frequently overlook syntactic cues, a shortcoming less present in BERT-based architectures.
Pretrained Language Models v. Court Ruling Predictions: A Case Study on a Small Dataset of French Court of Appeal Rulings
Authors: Olivia Vaudaux; Caroline Bazzoli; Maximin Coavoux; Géraldine Vial; Étienne Vergès
Venue: EMNLP 2023 (Proceedings of the Natural Legal Language Processing Workshop 2023)

Abstract: NLP systems are increasingly used in the law domain, either by legal institutions or by the industry. As a result there is a pressing need to characterize their strengths and weaknesses and understand their inner workings. This article presents a case study on the task of judicial decision prediction, on a small dataset from French Courts of Appeal. Specifically, our dataset of around 1000 decisions is about the habitual place of residency of children from divorced parents. The task consists in predicting, from the facts and reasons of the documents, whether the court rules that children should live with their mother or their father. Instead of feeding the whole document to a classifier, we carefully construct the dataset to make sure that the input to the classifier does not contain any ‘spoilers’ (it is often the case in court rulings that information all along the document mentions the final decision). Our results are mostly negative: even classifiers based on French pretrained language models (Flaubert, JuriBERT) do not classify the decisions with a reasonable accuracy. However, they can extract the decision when it is part of the input. With regards to these results, we argue that there is a strong caveat when constructing legal NLP datasets automatically.
Italian Legislative Text Classification for Gazzetta Ufficiale
Authors: Marco Rovera; Alessio Palmero Aprosio; Francesco Greco; Mariano Lucchese; Sara Tonelli; Antonio Antetomaso
Venue: EMNLP 2023 (Proceedings of the Natural Legal Language Processing Workshop 2023)

Abstract: This work introduces a novel, extensive annotated corpus for multi-label legislative text classification in Italian, based on legal acts from the Gazzetta Ufficiale, the official source of legislative information of the Italian state. The annotated dataset, which we released to the community, comprises over 363,000 titles of legislative acts, spanning over 30 years from 1988 until 2022. Moreover, we evaluate four models for text classification on the dataset, demonstrating how using only the acts’ titles can achieve top-level classification performance, with a micro F1-score of 0.87. Also, our analysis shows how Italian domain-adapted legal models do not outperform general-purpose models on the task. Models’ performance can be checked by users via a demonstrator system provided in support of this work.
Mixed-domain Language Modeling for Processing Long Legal Documents
Authors: Wenyue Hua; Yuchen Zhang; Zhe Chen; Josie Li; Melanie Weber
Venue: EMNLP 2023 (Proceedings of the Natural Legal Language Processing Workshop 2023)

Abstract: The application of Natural Language Processing (NLP) to specialized domains, such as the law, has recently received a surge of interest. As many legal services rely on processing and analyzing large collections of documents, automating such tasks with NLP tools such as language models emerges as a key challenge since legal documents may contain specialized vocabulary from other domains, such as medical terminology in personal injury text. However, most language models are general-purpose models, which either have limited reasoning capabilities on highly specialized legal terminology and syntax, such as BERT or ROBERTA, or are expensive to run and tune, such as GPT-3.5 and Claude. Thus, in this paper, we propose a specialized language model for personal injury text, LEGALRELECTRA, which is trained on mixed-domain legal and medical corpora. We show that as a small language model, our model improves over general-domain and single-domain medical and legal language models when processing mixed-domain (personal injury) text. Our training architecture implements the ELECTRA framework but utilizes REFORMER instead of BERT for its generator and discriminator. We show that this improves the model’s performance on processing long passages and results in better long-range text comprehension.