To this date, the efficacy of the scientific publishing enterprise fundamentally rests on the strength of the peer review process. The journal editor or the conference chair primarily relies on the expert reviewers’ assessment, identify points of agreement and disagreement and try to reach a consensus to make a fair and informed decision on whether to accept or reject a paper. However, with the escalating number of submissions requiring review, especially in top-tier Artificial Intelligence (AI) conferences, the editor/chair, among many other works, invests a significant, sometimes stressful effort to mitigate reviewer disagreements. Here in this work, we introduce a novel task of automatically identifying contradictions among reviewers on a given article. To this end, we introduce ContraSciView, a comprehensive review-pair contradiction dataset on around 8.5k papers (with around 28k review pairs containing nearly 50k review pair comments) from the open review-based ICLR and NeurIPS conferences. We further propose a baseline model that detects contradictory statements from the review pairs. To the best of our knowledge, we make the first attempt to identify disagreements among peer reviewers automatically. We make our dataset and code public for further investigations.

In this article, we report the findings of the second shared task on Automatic Minuting (AutoMin) held as a Generation Challenge at the 16th International Natural Language Generation (INLG) Conference 2023. The second Automatic Minuting shared task is a successor to the first AutoMin which took place in 2021. The primary objective of the AutoMin shared task is to garner participation of the speech and natural language processing and generation community to create automatic methods for generating minutes from multi-party meetings. Five teams from diverse backgrounds participated in the shared task this year. A lot has changed in the Generative AI landscape since the last AutoMin especially with the emergence and wide adoption of Large Language Models (LLMs) to different downstream tasks. Most of the contributions are based on some form of an LLM and we are also adding current outputs of GPT4 as a benchmark. Furthermore, we examine the applicability of GPT-4 for automatic scoring of minutes. Compared to the previous instance of AutoMin, we also add another domain, the minutes for EU Parliament sessions, and we experiment with a more fine-grained manual evaluation. More details on the event can be found at https://ufal.github.io/automin-2023/.

Taking minutes is an essential component of every meeting, although the goals, style, and procedure of this activity (“minuting” for short) can vary. Minuting is a rather unstructured writing activity and is affected by who is taking the minutes and for whom the intended minutes are. With the rise of online meetings, automatic minuting would be an important benefit for the meeting participants as well as for those who might have missed the meeting. However, automatically generating meeting minutes is a challenging problem due to a variety of factors including the quality of automatic speech recorders (ASRs), availability of public meeting data, subjective knowledge of the minuter, etc. In this work, we present the first of its kind dataset on Automatic Minuting. We develop a dataset of English and Czech technical project meetings which consists of transcripts generated from ASRs, manually corrected, and minuted by several annotators. Our dataset, AutoMin, consists of 113 (English) and 53 (Czech) meetings, covering more than 160 hours of meeting content. Upon acceptance, we will publicly release (aaa.bbb.ccc) the dataset as a set of meeting transcripts and minutes, excluding the recordings for privacy reasons. A unique feature of our dataset is that most meetings are equipped with more than one minute, each created independently. Our corpus thus allows studying differences in what people find important while taking the minutes. We also provide baseline experiments for the community to explore this novel problem further. To the best of our knowledge AutoMin is probably the first resource on minuting in English and also in a language other than English (Czech).

This work represents the system proposed by team Innovators for SemEval 2022 Task 8: Multilingual News Article Similarity. Similar multilingual news articles should match irrespective of the style of writing, the language of conveyance, and subjective decisions and biases induced by medium/outlet. The proposed architecture includes a machine translation system that translates multilingual news articles into English and presents a multitask learning model trained simultaneously on three distinct datasets. The system leverages the PageRank algorithm for Long-form text alignment. Multitask learning approach allows simultaneous training of multiple tasks while sharing the same encoder during training, facilitating knowledge transfer between tasks. Our best model is ranked 16 with a Pearson score of 0.733.

The peer-review system has primarily remained the central process of all science communications. However, research has shown that the process manifests a power-imbalance scenario where the reviewer enjoys a position where their comments can be overly critical and wilfully obtuse without being held accountable. This brings into question the sanctity of the peer-review process, turning it into a fraught and traumatic experience for authors. A little more effort to still remain critical but be constructive in the feedback would help foster a progressive outcome from the peer-review process. In this paper, we argue to intervene at the step where this power imbalance actually begins in the system. To this end, we develop the first dataset of peer-review comments with their real-valued harshness scores. We build our dataset by using the popular Best-Worst-Scaling mechanism. We show the utility of our dataset for text moderation in peer reviews to make review reports less hurtful and more welcoming. We release our dataset and associated codes in https://github.com/Tirthankar-Ghosal/moderating-peer-review-harshness. Our research is one step towards helping create constructive peer-review reports.

We would host the AutoMin generation chal- lenge at INLG 2023 as a follow-up of the first AutoMin shared task at Interspeech 2021. Our shared task primarily concerns the automated generation of meeting minutes from multi-party meeting transcripts. In our first venture, we ob- served the difficulty of the task and highlighted a number of open problems for the community to discuss, attempt, and solve. Hence, we invite the Natural Language Generation (NLG) com- munity to take part in the second iteration of AutoMin. Like the first, the second AutoMin will feature both English and Czech meetings and the core task of summarizing the manually- revised transcripts into bulleted minutes. A new challenge we are introducing this year is to devise efficient metrics for evaluating the quality of minutes. We will also host an optional track to generate minutes for European parliamentary sessions. We carefully curated the datasets for the above tasks. Our ELITR Minuting Corpus has been recently accepted to LREC 2022 and publicly released. We are already preparing a new test set for evaluating the new shared tasks. We hope to carry forward the learning from the first AutoMin and instigate more community attention and interest in this timely yet chal- lenging problem. INLG, the premier forum for the NLG community, would be an appropriate venue to discuss the challenges and future of Automatic Minuting. The main objective of the AutoMin GenChal at INLG 2023 would be to come up with efficient methods to auto- matically generate meeting minutes and design evaluation metrics to measure the quality of the minutes.

We describe our multi-task learning based ap- proach for summarization of real-life dialogues as part of the DialogSum Challenge shared task at INLG 2022. Our approach intends to im- prove the main task of abstractive summariza- tion of dialogues through the auxiliary tasks of extractive summarization, novelty detection and language modeling. We conduct extensive experimentation with different combinations of tasks and compare the results. In addition, we also incorporate the topic information provided with the dataset to perform topic-aware sum- marization. We report the results of automatic evaluation of the generated summaries in terms of ROUGE and BERTScore.

Peer reviews are intended to give authors constructive and informative feedback. It is expected that the reviewers will make constructive suggestions over certain aspects, e.g., novelty, clarity, empirical and theoretical soundness, etc., and sections, e.g., problem definition/idea, datasets, methodology, experiments, results, etc., of the paper in a detailed manner. With this objective, we analyze the reviewer’s attitude towards the work. Aspects of the review are essential to determine how much weight the editor/chair should place on the review in making a decision. In this paper, we used a publically available Peer Review Analyze dataset of peer review texts manually annotated at the sentence level (∼13.22 k sentences) across two layers:Paper Section Correspondence and Paper Aspect Category. We transform these categorical annotations to derive an informativeness score of the review based on the review’s coverage across section correspondence, aspects of the paper, and reviewer-centric uncertainty associated with the review. We hope that our proposed methods, which are motivated towards automatically estimating the quality of peer reviews in the form of informativeness scores, will give editors an additional layer of confidence for the automatic judgment of review quality. We make our codes available at https://github.com/PrabhatkrBharti/informativeness.git.

The growth of multilingual web content in low-resource languages is becoming an emerging challenge to detect misinformation. One particular hindrance to research on this problem is the non-availability of resources and tools. Majority of the earlier works in misinformation detection are based on English content which confines the applicability of the research to a specific language only. Increasing presence of multimedia content on the web has promoted misinformation in which real multimedia content (images, videos) are used in different but related contexts with manipulated texts to mislead the readers. Detecting this category of misleading information is almost impossible without any prior knowledge. Studies say that emotion-invoking and highly novel content accelerates the dissemination of false information. To counter this problem, here in this paper, we first introduce a novel multilingual multimodal misinformation dataset that includes background knowledge (from authentic sources) of the misleading articles. Second, we propose an effective neural model leveraging novelty detection and emotion recognition to detect fabricated information. We perform extensive experiments to justify that our proposed model outperforms the state-of-the-art (SOTA) on the concerned task.

The quest for new information is an inborn human trait and has always been quintessential for human survival and progress. Novelty drives curiosity, which in turn drives innovation. In Natural Language Processing (NLP), Novelty Detection refers to finding text that has some new information to offer with respect to whatever is earlier seen or known. With the exponential growth of information all across the Web, there is an accompanying menace of redundancy. A considerable portion of the Web contents are duplicates, and we need efficient mechanisms to retain new information and filter out redundant information. However, detecting redundancy at the semantic level and identifying novel text is not straightforward because the text may have less lexical overlap yet convey the same information. On top of that, non-novel/redundant information in a document may have assimilated from multiple source documents, not just one. The problem surmounts when the subject of the discourse is documents, and numerous prior documents need to be processed to ascertain the novelty/non-novelty of the current one in concern. In this work, we build upon our earlier investigations for document-level novelty detection and present a comprehensive account of our efforts toward the problem. We explore the role of pre-trained Textual Entailment (TE) models to deal with multiple source contexts and present the outcome of our current investigations. We argue that a multipremise entailment task is one close approximation toward identifying semantic-level non-novelty. Our recent approach either performs comparably or achieves significant improvement over the latest reported results on several datasets and across several related tasks (paraphrasing, plagiarism, rewrite). We critically analyze our performance with respect to the existing state of the art and show the superiority and promise of our approach for future investigations. We also present our enhanced dataset TAP-DLND 2.0 and several baselines to the community for further research on document-level novelty detection.

In this article, we describe the overview of our shared task: Detecting Entities in the Astrophysics Literature (DEAL). The DEAL shared task was part of the Workshop on Information Extraction from Scientific Publications (WIESP) in AACL-IJCNLP 2022. Information extraction from scientific publications is critical in several downstream tasks such as identification of critical entities, article summarization, citation classification, etc. The motivation of this shared task was to develop a community-wide effort for entity extraction from astrophysics literature. Automated entity extraction would help to build knowledge bases, high-quality meta-data for indexing and search, and several other use-cases of interests. Thirty-three teams registered for DEAL, twelve of them participated in the system runs, and finally four teams submitted their system descriptions. We analyze their system and performance and finally discuss the findings of DEAL.

With the ever-increasing pace of research and high volume of scholarly communication, scholars face a daunting task. Not only must they keep up with the growing literature in their own and related fields, scholars increasingly also need to rebut pseudo-science and disinformation. These needs have motivated an increasing focus on computational methods for enhancing search, summarization, and analysis of scholarly documents. However, the various strands of research on scholarly document processing remain fragmented. To reach out to the broader NLP and AI/ML community, pool distributed efforts in this area, and enable shared access to published research, we held the 3rd Workshop on Scholarly Document Processing (SDP) at COLING as a hybrid event (https://sdproc.org/2022/). The SDP workshop consisted of a research track, three invited talks and five Shared Tasks: 1) MSLR22: Multi-Document Summarization for Literature Reviews, 2) DAGPap22: Detecting automatically generated scientific papers, 3) SV-Ident 2022: Survey Variable Identification in Social Science Publications, 4) SKGG: Scholarly Knowledge Graph Generation, 5) MuP 2022: Multi Perspective Scientific Document Summarization. The program was geared towards NLP, information retrieval, and data mining for scholarly documents, with an emphasis on identifying and providing solutions to open challenges.

Research in the biomedical domain is con- stantly challenged by its large amount of ever- evolving textual information. Biomedical re- searchers are usually required to conduct a lit- erature review before any medical interven- tion to assess the effectiveness of the con- cerned research. However, the process is time- consuming, and therefore, automation to some extent would help reduce the accompanying information overload. Multi-document sum- marization of scientific articles for literature reviews is one approximation of such automa- tion. Here in this paper, we describe our pipelined approach for the aforementioned task. We design a BERT-based extractive method followed by a BigBird PEGASUS-based ab- stractive pipeline for generating literature re- view summaries from the abstracts of biomedi- cal trial reports as part of the Multi-document Summarization for Literature Review (MSLR) shared task1 in the Scholarly Document Pro- cessing (SDP) workshop 20222. Our proposed model achieves the best performance on the MSLR-Cochrane leaderboard3 on majority of the evaluation metrics. Human scrutiny of our automatically generated summaries indicates that our approach is promising to yield readable multi-article summaries for conducting such lit- erature reviews.

We present the main findings of MuP 2022 shared task, the first shared task on multi-perspective scientific document summarization. The task provides a testbed representing challenges for summarization of scientific documents, and facilitates development of better models to leverage summaries generated from multiple perspectives. We received 139 total submissions from 9 teams. We evaluated submissions both by automated metrics (i.e., Rouge) and human judgments on faithfulness, coverage, and readability which provided a more nuanced view of the differences between the systems. While we observe encouraging results from the participating teams, we conclude that there is still significant room left for improving summarization leveraging multiple references. Our dataset is available at https://github.com/allenai/mup.

Argument mining targets structures in natural language related to interpretation and persuasion which are central to scientific communication. Most scholarly discourse involves interpreting experimental evidence and attempting to persuade other scientists to adopt the same conclusions. While various argument mining studies have addressed student essays and news articles, those that target scientific discourse are still scarce. This paper surveys existing work in argument mining of scholarly discourse, and provides an overview of current models, data, tasks, and applications. We identify a number of key challenges confronting argument mining in the scientific domain, and suggest some possible solutions and future directions.

Citations are crucial to a scientific discourse. Besides providing additional contexts to research papers, citations act as trackers of the direction of research in a field and as an important measure in understanding the impact of a research publication. With the rapid growth in research publications, automated solutions for identifying the purpose and influence of citations are becoming very important. The 3C Citation Context Classification Task organized as part of the Second Workshop on Scholarly Document Processing @ NAACL 2021 is a shared task to address the aforementioned problems. In this paper, we present our team, IITP-CUNI@3C’s submission to the 3C shared tasks. For Task A, citation context purpose classification, we propose a neural multi-task learning framework that harnesses the structural information of the research papers and the relation between the citation context and the cited paper for citation classification. For Task B, citation context influence classification, we use a set of simple features to classify citations based on their perceived significance. We achieve comparable performance with respect to the best performing systems in Task A and superseded the majority baseline in Task B with very simple features.

With the ever-increasing pace of research and high volume of scholarly communication, scholars face a daunting task. Not only must they keep up with the growing literature in their own and related fields, scholars increasingly also need to rebut pseudo-science and disinformation. These needs have motivated an increasing focus on computational methods for enhancing search, summarization, and analysis of scholarly documents. However, the various strands of research on scholarly document processing remain fragmented. To reach out to the broader NLP and AI/ML community, pool distributed efforts in this area, and enable shared access to published research, we held the 2nd Workshop on Scholarly Document Processing (SDP) at NAACL 2021 as a virtual event (https://sdproc.org/2021/). The SDP workshop consisted of a research track, three invited talks, and three Shared Tasks (LongSumm 2021, SCIVER, and 3C). The program was geared towards the application of NLP, information retrieval, and data mining for scholarly documents, with an emphasis on identifying and providing solutions to open challenges.

In this work, we describe our system submission to the SemEval 2021 Task 11: NLP Contribution Graph Challenge. We attempt all the three sub-tasks in the challenge and report our results. Subtask 1 aims to identify the contributing sentences in a given publication. Subtask 2 follows from Subtask 1 to extract the scientific term and predicate phrases from the identified contributing sentences. The final Subtask 3 entails extracting triples (subject, predicate, object) from the phrases and categorizing them under one or more defined information units. With the NLPContributionGraph Shared Task, the organizers formalized the building of a scholarly contributions-focused graph over NLP scholarly articles as an automated task. Our approaches include a BERT-based classification model for identifying the contributing sentences in a research publication, a rule-based dependency parsing for phrase extraction, followed by a CNN-based model for information units classification, and a set of rules for triples extraction. The quantitative results show that we obtain the 5th, 5th, and 7th rank respectively in three evaluation phases. We make our codes available at https://github.com/HardikArora17/SemEval-2021-INNOVATORS.

Next to keeping up with the growing literature in their own and related fields, scholars increasingly also need to rebut pseudo-science and disinformation. To address these challenges, computational work on enhancing search, summarization, and analysis of scholarly documents has flourished. However, the various strands of research on scholarly document processing remain fragmented. To reach to the broader NLP and AI/ML community, pool distributed efforts and enable shared access to published research, we held the 1st Workshop on Scholarly Document Processing at EMNLP 2020 as a virtual event. The SDP workshop consisted of a research track (including a poster session), two invited talks and three Shared Tasks (CL-SciSumm, Lay-Summ and LongSumm), geared towards easier access to scientific methods and results. Website: https://ornlcda.github.io/SDProc

Automatically validating a research artefact is one of the frontiers in Artificial Intelligence (AI) that directly brings it close to competing with human intellect and intuition. Although criticised sometimes, the existing peer review system still stands as the benchmark of research validation. The present-day peer review process is not straightforward and demands profound domain knowledge, expertise, and intelligence of human reviewer(s), which is somewhat elusive with the current state of AI. However, the peer review texts, which contains rich sentiment information of the reviewer, reflecting his/her overall attitude towards the research in the paper, could be a valuable entity to predict the acceptance or rejection of the manuscript under consideration. Here in this work, we investigate the role of reviewer sentiment embedded within peer review texts to predict the peer review outcome. Our proposed deep neural architecture takes into account three channels of information: the paper, the corresponding reviews, and review’s polarity to predict the overall recommendation score as well as the final decision. We achieve significant performance improvement over the baselines (∼ 29% error reduction) proposed in a recently released dataset of peer reviews. An AI of this kind could assist the editors/program chairs as an additional layer of confidence, especially when non-responding/missing reviewers are frequent in present day peer review.

The rapid growth of documents across the web has necessitated finding means of discarding redundant documents and retaining novel ones. Capturing redundancy is challenging as it may involve investigating at a deep semantic level. Techniques for detecting such semantic redundancy at the document level are scarce. In this work we propose a deep Convolutional Neural Networks (CNN) based model to classify a document as novel or redundant with respect to a set of relevant documents already seen by the system. The system is simple and do not require any manual feature engineering. Our novel scheme encodes relevant and relative information from both source and target texts to generate an intermediate representation which we coin as the Relative Document Vector (RDV). The proposed method outperforms the existing state-of-the-art on a document-level novelty detection dataset by a margin of ∼5% in terms of accuracy. We further demonstrate the effectiveness of our approach on a standard paraphrase detection dataset where paraphrased passages closely resemble to semantically redundant documents.