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Found 21 papers, 6 papers with code
Overview of the Third Workshop on Scholarly Document Processing
With the ever-increasing pace of research and high volume of scholarly communication, scholars face a daunting task.
Overview of the First Shared Task on Multi Perspective Scientific Document Summarization (MuP)
We present the main findings of MuP 2022 shared task, the first shared task on multi-perspective scientific document summarization.
Overview of the Second Workshop on Scholarly Document Processing
With the ever-increasing pace of research and high volume of scholarly communication, scholars face a daunting task.
Overview of the First Workshop on Scholarly Document Processing (SDP)
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.
Overview and Insights from the Shared Tasks at Scholarly Document Processing 2020: CL-SciSumm, LaySumm and LongSumm
We present the results of three Shared Tasks held at the Scholarly Document Processing Workshop at EMNLP2020: CL-SciSumm, LaySumm and LongSumm.
Stay Tuned: An Empirical Study of the Impact of Hyperparameters on LLM Tuning in Real-World Applications
Here, we provide recommended HP configurations for practical use-cases that represent a better starting point for practitioners, when considering two SOTA LLMs and two commonly used tuning methods.
Do These LLM Benchmarks Agree? Fixing Benchmark Evaluation with BenchBench
Despite the crucial role of BAT for benchmark builders and consumers, there are no standardized procedures for such agreement testing.
Unitxt: Flexible, Shareable and Reusable Data Preparation and Evaluation for Generative AI
In the dynamic landscape of generative NLP, traditional text processing pipelines limit research flexibility and reproducibility, as they are tailored to specific dataset, task, and model combinations.
Efficient Benchmarking of Language Models
The increasing versatility of language models (LMs) has given rise to a new class of benchmarks that comprehensively assess a broad range of capabilities.
Active Learning for Natural Language Generation
In this paper, we present a first systematic study of active learning for NLG, considering a diverse set of tasks and multiple leading selection strategies, and harnessing a strong instruction-tuned model.
nBIIG: A Neural BI Insights Generation System for Table Reporting
We present nBIIG, a neural Business Intelligence (BI) Insights Generation system.
Diversity Enhanced Table-to-Text Generation via Type Control
Generating natural language statements to convey logical inferences from tabular data (i. e., Logical NLG) is a process with one input and a variety of valid outputs.
Quality Controlled Paraphrase Generation
Furthermore, we suggest a method that given a sentence, identifies points in the quality control space that are expected to yield optimal generated paraphrases.
HowSumm: A Multi-Document Summarization Dataset Derived from WikiHow Articles
We present HowSumm, a novel large-scale dataset for the task of query-focused multi-document summarization (qMDS), which targets the use-case of generating actionable instructions from a set of sources.
Ranked #1 on
Document Summarization
on HowSumm-Step
orgFAQ: A New Dataset and Analysis on Organizational FAQs and User Questions
Thus, we release orgFAQ, a new dataset composed of $6988$ user questions and $1579$ corresponding FAQs that were extracted from organizations' FAQ webpages in the Jobs domain.
A Summarization System for Scientific Documents
We present a novel system providing summaries for Computer Science publications.
TalkSumm: A Dataset and Scalable Annotation Method for Scientific Paper Summarization Based on Conference Talks
We collected 1716 papers and their corresponding videos, and created a dataset of paper summaries.
Bot2Vec: Learning Representations of Chatbots
Chatbots (i. e., bots) are becoming widely used in multiple domains, along with supporting bot programming platforms.
Detecting Egregious Conversations between Customers and Virtual Agents
In this paper, we outline an approach to detecting such egregious conversations, using behavioral cues from the user, patterns in agent responses, and user-agent interaction.
Neural Response Generation for Customer Service based on Personality Traits
We present a neural response generation model that generates responses conditioned on a target personality.
