Large Language Models (LLMs) have demonstrated impressive performance across
a wide range of NLP tasks, including summarization. Inherently LLMs produce
abstractive summaries, and the task of achieving extractive summaries through
LLMs still remains largely unexplored. To bridge this gap, in this work, we
propose a novel framework LaMSUM to generate extractive summaries through LLMs
for large user-generated text by leveraging voting algorithms. Our evaluation
on three popular open-source LLMs (Llama 3, Mixtral and Gemini) reveal that the
LaMSUM outperforms state-of-the-art extractive summarization methods. We
further attempt to provide the rationale behind the output summary produced by
LLMs. Overall, this is one of the early attempts to achieve extractive
summarization for large user-generated text by utilizing LLMs, and likely to
generate further interest in the community.

这项研究通过利用投票算法，提出了一种新的框架 LaMSUM，通过大型语言模型生成用户生成的文本的摘要，结果显示 LaMSUM 优于现有的提取式摘要方法，同时试图解释语言模型生成的摘要产生的原因。

LaMSUM: 一种使用 LLMs 的用户生成内容抽取式摘要的新框架

LaMSUM: A Novel Framework for Extractive Summarization of User Generated  Content using LLMs

The successful analysis of argumentative techniques from user-generated text
is central to many downstream tasks such as political and market analysis.
Recent argument mining tools use state-of-the-art deep learning methods to
extract and annotate argumentative techniques from various online text corpora,
however each task is treated as separate and different bespoke models are
fine-tuned for each dataset. We show that different argument mining tasks share
common semantic and logical structure by implementing a multi-task approach to
argument mining that achieves better performance than state-of-the-art methods
for the same problems. Our model builds a shared representation of the input
text that is common to all tasks and exploits similarities between tasks in
order to further boost performance via parameter-sharing. Our results are
important for argument mining as they show that different tasks share
substantial similarities and suggest a holistic approach to the extraction of
argumentative techniques from text.

该研究使用多任务学习方法将不同的论证挖掘任务相结合，利用语义和逻辑结构的相似性来提高性能，从而揭示了论证挖掘任务之间的共性，为文本中论证技巧的提取提供了整体方法。

多任务学习提高深度论证挖掘模型性能

Multi-Task Learning Improves Performance In Deep Argument Mining Models

Since BERT appeared, Transformer language models and transfer learning have
become state-of-the-art for Natural Language Understanding tasks. Recently,
some works geared towards pre-training specially-crafted models for particular
domains, such as scientific papers, medical documents, user-generated texts,
among others. These domain-specific models have been shown to improve
performance significantly in most tasks. However, for languages other than
English such models are not widely available.
In this work, we present RoBERTuito, a pre-trained language model for
user-generated text in Spanish, trained on over 500 million tweets. Experiments
on a benchmark of tasks involving user-generated text showed that RoBERTuito
outperformed other pre-trained language models in Spanish. In addition to this,
our model achieves top results for some English-Spanish tasks of the Linguistic
Code-Switching Evaluation benchmark (LinCE) and has also competitive
performance against monolingual models in English tasks. To facilitate further
research, we make RoBERTuito publicly available at the HuggingFace model hub
together with the dataset used to pre-train it.

该研究介绍了 RoBERTuito，这是一个用于西班牙语用户生成文本的预训练语言模型，通过在超过 5 亿条推文上训练，RoBERTuito 在涉及用户生成文本的基准任务中表现优异，并取得了与英语单语模型相竞争的成绩。

RoBERTuito：面向西班牙社交媒体文本的预训练语言模型

RoBERTuito: a pre-trained language model for social media text in  Spanish

We investigate using Named Entity Recognition on a new type of user-generated
text: a call center conversation. These conversations combine problems from
spontaneous speech with problems novel to conversational Automated Speech
Recognition, including incorrect recognition, alongside other common problems
from noisy user-generated text. Using our own corpus with new annotations,
training custom contextual string embeddings, and applying a BiLSTM-CRF, we
match state-of-the-art results on our novel task.

研究使用命名实体识别技术处理新型用户生成的文本：客户服务会话记录，使用自身语料库，训练上下文字符串嵌入，应用 BiLSTM-CRF 算法，取得了该任务的最新最优结果。