Conversational information-seeking (CIS) is an emerging paradigm for
knowledge acquisition and exploratory search. Traditional web search interfaces
enable easy exploration of entities, but this is limited in conversational
settings due to the limited-bandwidth interface. This paper explore ways to
rewrite answers in CIS, so that users can understand them without having to
resort to external services or sources. Specifically, we focus on salient
entities -- entities that are central to understanding the answer. As our first
contribution, we create a dataset of conversations annotated with entities for
saliency. Our analysis of the collected data reveals that the majority of
answers contain salient entities. As our second contribution, we propose two
answer rewriting strategies aimed at improving the overall user experience in
CIS. One approach expands answers with inline definitions of salient entities,
making the answer self-contained. The other approach complements answers with
follow-up questions, offering users the possibility to learn more about
specific entities. Results of a crowdsourcing-based study indicate that
rewritten answers are clearly preferred over the original ones. We also find
that inline definitions tend to be favored over follow-up questions, but this
choice is highly subjective, thereby providing a promising future direction for
personalization.

对话式信息获取是一种新兴的知识获取和探索性搜索范式，本论文旨在探索对话式信息获取中的答案重写方法，使用户能够理解答案而无需借助外部服务或来源。通过创建带有突出实体注释的对话数据集，该研究发现大多数答案包含重要实体。提出了两种旨在改善对话式信息获取中用户体验的答案重写策略，其中一种方法是通过内联定义重写答案，使答案在自身中包含所述重要实体的定义；另一种方法是通过补充后续问题来补充答案，使用户可以进一步了解特定实体。众包研究结果表明，重写后的答案明显优于原始答案。同时发现内联定义往往优于后续问题，但这种选择高度主观，这为个性化提供了一个有希望的未来方向。

迈向自动独立答案：基于实体的对话式搜索答案重写

Towards Self-Contained Answers: Entity-Based Answer Rewriting in  Conversational Search

Clinician must write a lengthy summary each time a patient is discharged from
the hospital. This task is time-consuming due to the sheer number of unique
clinical concepts covered in the admission. Identifying and covering salient
entities is vital for the summary to be clinically useful. We fine-tune
open-source LLMs (Mistral-7B-Instruct and Zephyr-7B-\b{eta}) on the task and
find that they generate incomplete and unfaithful summaries. To increase entity
coverage, we train a smaller, encoder-only model to predict salient entities,
which are treated as content-plans to guide the LLM. To encourage the LLM to
focus on specific mentions in the source notes, we propose SPEER:
Sentence-level Planning via Embedded Entity Retrieval. Specifically, we mark
each salient entity span with special "{{ }}" boundary tags and instruct the
LLM to retrieve marked spans before generating each sentence. Sentence-level
planning acts as a form of state tracking in that the model is explicitly
recording the entities it uses. We fine-tune Mistral and Zephyr variants on a
large-scale, diverse dataset of ~167k in-patient hospital admissions and
evaluate on 3 datasets. SPEER shows gains in both coverage and faithfulness
metrics over non-guided and guided baselines.

通过训练较小的编码器模型预测显著实体，使用 SPEER 进行句子级规划以增强摘要的内容涵盖度和准确性。

SPEER：通过嵌入式实体检索进行长临床摘要的句子级规划

SPEER: Sentence-Level Planning of Long Clinical Summaries via Embedded  Entity Retrieval

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. These
entities, often deemed as salient entities, provide useful cues of the
aboutness of a document to a reader. Identifying the salience of entities was
found helpful in several downstream applications such as search, ranking, and
entity-centric summarization, among others. Prior work on salient entity
detection mainly focused on machine learning models that require heavy feature
engineering. We show that fine-tuning medium-sized language models with a
cross-encoder style architecture yields substantial performance gains over
feature engineering approaches. To this end, we conduct a comprehensive
benchmarking of four publicly available datasets using models representative of
the medium-sized pre-trained language model family. Additionally, we show that
zero-shot prompting of instruction-tuned language models yields inferior
results, indicating the task's uniqueness and complexity.

在文本文档中，通过对中的有限实体进行鲜明实体识别可以在多个下游应用领域中实现重要性提取，本研究通过细调中型语言模型以交叉编码器结构取得重大性能提升，同时展示了零 - shot 指令调整语言模型在此任务上表现不佳，凸显其独特且复杂性质。