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Found 12 papers, 5 papers with code
EntSUM: A Data Set for Entity-Centric Extractive Summarization
Controllable summarization aims to provide summaries that take into account user-specified aspects and preferences to better assist them with their information need, as opposed to the standard summarization setup which build a single generic summary of a document. We introduce a human-annotated data set EntSUM for controllable summarization with a focus on named entities as the aspects to control. We conduct an extensive quantitative analysis to motivate the task of entity-centric summarization and show that existing methods for controllable summarization fail to generate entity-centric summaries.
Leveraging Contextual Information for Effective Entity Salience Detection
In text documents such as news articles, the content and key events usually revolve around a subset of all the entities mentioned in a document.
Unsupervised Contrast-Consistent Ranking with Language Models
We compare pairwise, pointwise and listwise prompting techniques to elicit a language model's ranking knowledge.
Overcoming Catastrophic Forgetting in Massively Multilingual Continual Learning
Real-life multilingual systems should be able to efficiently incorporate new languages as data distributions fed to the system evolve and shift over time.
Dataless Knowledge Fusion by Merging Weights of Language Models
In this paper, we study the problem of merging individual models built on different training data sets to obtain a single model that performs well both across all data set domains and can generalize on out-of-domain data.
EntSUM: A Data Set for Entity-Centric Summarization
Our analysis and results show the challenging nature of this task and of the proposed data set.
Identifying Named Entities as they are Typed
However, the typical experimental setup for evaluating Named Entity Recognition (NER) systems is not directly applicable to systems that process text in real time as the text is being typed.
Fact vs. Opinion: the Role of Argumentation Features in News Classification
A 2018 study led by the Media Insight Project showed that most journalists think that a clearmarking of what is news reporting and what is commentary or opinion (e. g., editorial, op-ed)is essential for gaining public trust.
Multi-Domain Named Entity Recognition with Genre-Aware and Agnostic Inference
Named entity recognition is a key component of many text processing pipelines and it is thus essential for this component to be robust to different types of input.
Temporally-Informed Analysis of Named Entity Recognition
Natural language processing models often have to make predictions on text data that evolves over time as a result of changes in language use or the information described in the text.
Analyzing Political Parody in Social Media
Parody is a figurative device used to imitate an entity for comedic or critical purposes and represents a widespread phenomenon in social media through many popular parody accounts.
Automatically Identifying Complaints in Social Media
Complaining is a basic speech act regularly used in human and computer mediated communication to express a negative mismatch between reality and expectations in a particular situation.
