The methods of automatic speech summarization are classified into two groups:
supervised and unsupervised methods. Supervised methods are based on a set of
features, while unsupervised methods perform summarization based on a set of
rules. Latent Semantic Analysis (LSA) and Maximal Marginal Relevance (MMR) are
considered the most important and well-known unsupervised methods in automatic
speech summarization. This study set out to investigate the performance of two
aforementioned unsupervised methods in transcriptions of Persian broadcast news
summarization. The results show that in generic summarization, LSA outperforms
MMR, and in query-based summarization, MMR outperforms LSA in broadcast news
summarization.

本研究探讨了两种无监督方法自动语音摘要的表现，使用了 LSA 和 MMR 这两种方法对波斯广播新闻的摘要进行了比较，结果表明 LSA 在通用摘要上优于 MMR，但在基于查询的摘要中，MMR 优于 LSA。

无监督广播新闻摘要：最大边际相关性（MMR）和潜在语义分析（LSA）的比较研究

Unsupervised Broadcast News Summarization; a comparative study on Maximal Marginal Relevance (MMR) and Latent Semantic Analysis (LSA)

The trade-off between relevance and fairness in personalized recommendations
has been explored in recent works, with the goal of minimizing learned
discrimination towards certain demographics while still producing relevant
results.
We present a fairness-aware variation of the Maximal Marginal Relevance (MMR)
re-ranking method which uses representations of demographic groups computed
using a labeled dataset. This method is intended to incorporate fairness with
respect to these demographic groups.
We perform an experiment on a stock photo dataset and examine the trade-off
between relevance and fairness against a well known baseline, MMR, by using
human judgment to examine the results of the re-ranking when using different
fractions of a labeled dataset, and by performing a quantitative analysis on
the ranked results of a set of query images. We show that our proposed method
can incorporate fairness in the ranked results while obtaining higher precision
than the baseline, while our case study shows that even a limited amount of
labeled data can be used to compute the representations to obtain fairness.
This method can be used as a post-processing step for recommender systems and
search.

本文提出了一种考虑公平性的最大边际相关方法 (MMR) 的变种，使用标记的数据集计算人口组的表示，来在相关性和公平性之间找到平衡。使用股票照片数据集进行实验，并通过人类评价和定量分析等方法，和已知的基准方法进行比较，表明我们提出的方法可以在获取更高精度的同时，在排名结果中获得公平性。从本质上讲，这种方法可以作为推荐系统和搜索的后处理步骤。

使用图像公平性表征在多样性排序中用于推荐

Using Image Fairness Representations in Diversity-Based Re-ranking for  Recommendations

Keyphrase extraction is the task of automatically selecting a small set of
phrases that best describe a given free text document. Supervised keyphrase
extraction requires large amounts of labeled training data and generalizes very
poorly outside the domain of the training data. At the same time, unsupervised
systems have poor accuracy, and often do not generalize well, as they require
the input document to belong to a larger corpus also given as input. Addressing
these drawbacks, in this paper, we tackle keyphrase extraction from single
documents with EmbedRank: a novel unsupervised method, that leverages sentence
embeddings. EmbedRank achieves higher F-scores than graph-based state of the
art systems on standard datasets and is suitable for real-time processing of
large amounts of Web data. With EmbedRank, we also explicitly increase coverage
and diversity among the selected keyphrases by introducing an embedding-based
maximal marginal relevance (MMR) for new phrases. A user study including over
200 votes showed that, although reducing the phrases' semantic overlap leads to
no gains in F-score, our high diversity selection is preferred by humans.

本文提出了一种新颖的无监督方法，即 EmbedRank，通过使用句子嵌入从单个文档中提取关键短语，进而在标准数据集上实现了比基于图的现有系统更高的 F-score，适用于实时处理大量 Web 数据，并通过引入基于嵌入的最大边际相关性来明确地增加所选关键短语的覆盖率和多样性，200 份投票的用户调查表明，我们的高多样性选择得到人类的接受。