E-commerce product catalogs contain billions of items. Most products have
lengthy titles, as sellers pack them with product attributes to improve
retrieval, and highlight key product aspects. This results in a gap between
such unnatural products titles, and how customers refer to them. It also limits
how e-commerce stores can use these seller-provided titles for recommendation,
QA, or review summarization.
Inspired by recent work on instruction-tuned LLMs, we present InstructPTS, a
controllable approach for the task of Product Title Summarization (PTS).
Trained using a novel instruction fine-tuning strategy, our approach is able to
summarize product titles according to various criteria (e.g. number of words in
a summary, inclusion of specific phrases, etc.). Extensive evaluation on a
real-world e-commerce catalog shows that compared to simple fine-tuning of
LLMs, our proposed approach can generate more accurate product name summaries,
with an improvement of over 14 and 8 BLEU and ROUGE points, respectively.

通过对商品标题进行指导性调优，我们提出了 InstructPTS，一种能够按照不同标准（如摘要中的字数、特定短语的包含等）总结产品标题的可控方法。在真实世界的电子商务目录上进行的大量评估表明，与仅使用 LLMs 进行简单调优相比，我们所提出的方法能够生成更准确的产品名称摘要，BLEU 和 ROUGE 分别提高了 14 个和 8 个分数。

InstructPTS：针对产品标题摘要的指导式调整语言模型

InstructPTS: Instruction-Tuning LLMs for Product Title Summarization

Product catalogs are valuable resources for eCommerce website. In the
catalog, a product is associated with multiple attributes whose values are
short texts, such as product name, brand, functionality and flavor. Usually
individual retailers self-report these key values, and thus the catalog
information unavoidably contains noisy facts. Although existing deep neural
network models have shown success in conducting cross-checking between two
pieces of texts, their success has to be dependent upon a large set of quality
labeled data, which are hard to obtain in this validation task: products span a
variety of categories. To address the aforementioned challenges, we propose a
novel meta-learning latent variable approach, called MetaBridge, which can
learn transferable knowledge from a subset of categories with limited labeled
data and capture the uncertainty of never-seen categories with unlabeled data.
More specifically, we make the following contributions. (1) We formalize the
problem of validating the textual attribute values of products from a variety
of categories as a natural language inference task in the few-shot learning
setting, and propose a meta-learning latent variable model to jointly process
the signals obtained from product profiles and textual attribute values. (2) We
propose to integrate meta learning and latent variable in a unified model to
effectively capture the uncertainty of various categories. (3) We propose a
novel objective function based on latent variable model in the few-shot
learning setting, which ensures distribution consistency between unlabeled and
labeled data and prevents overfitting by sampling from the learned
distribution. Extensive experiments on real eCommerce datasets from hundreds of
categories demonstrate the effectiveness of MetaBridge on textual attribute
validation and its outstanding performance compared with state-of-the-art
approaches.

MetaBridge 是一种新颖的元学习潜变量方法，可从有限标记数据的一组类别中学习可转移的知识，并利用未标记数据捕捉从未见过的类别的不确定性，用于验证产品目录中的文本属性值，具有比现有最先进方法更优异的性能。

通过有限标记数据学习实现电子商务目录文本属性值的自动验证

Automatic Validation of Textual Attribute Values in E-commerce Catalog  by Learning with Limited Labeled Data

Extraction of missing attribute values is to find values describing an
attribute of interest from a free text input. Most past related work on
extraction of missing attribute values work with a closed world assumption with
the possible set of values known beforehand, or use dictionaries of values and
hand-crafted features. How can we discover new attribute values that we have
never seen before? Can we do this with limited human annotation or supervision?
We study this problem in the context of product catalogs that often have
missing values for many attributes of interest.
In this work, we leverage product profile information such as titles and
descriptions to discover missing values of product attributes. We develop a
novel deep tagging model OpenTag for this extraction problem with the following
contributions: (1) we formalize the problem as a sequence tagging task, and
propose a joint model exploiting recurrent neural networks (specifically,
bidirectional LSTM) to capture context and semantics, and Conditional Random
Fields (CRF) to enforce tagging consistency, (2) we develop a novel attention
mechanism to provide interpretable explanation for our model's decisions, (3)
we propose a novel sampling strategy exploring active learning to reduce the
burden of human annotation. OpenTag does not use any dictionary or hand-crafted
features as in prior works. Extensive experiments in real-life datasets in
different domains show that OpenTag with our active learning strategy discovers
new attribute values from as few as 150 annotated samples (reduction in 3.3x
amount of annotation effort) with a high F-score of 83%, outperforming
state-of-the-art models.

本文提出了一种基于深度标注模型 OpenTag 的解决方案，可以利用商品标题和描述等信息，从产品目录中发现缺失值的属性值，通过为期更少的标注样本使用主动学习策略，OpenTag 已经成功地从 150 个标注样本中探索出新的属性值，并实现高 F 值 83％的表现