Keywords

1 Introduction

Multimedia content summarization is an important issue in recent years. Due to the great amount of information available in the web it is necessary to have different tools to help the users to digest that contents in an easy way. For this reason, summarization techniques are a current goal in Natural Language research [9, 14]. Traditionally, summarization methods are classified in two categories: extractive and abstractive. Extractive approaches consist of detecting the most salient sentences and the summary generated is composed by those sentences, while abstractive approaches try to be more similar to human summaries and they generate new sentences that may not be in the original document. Although, logically, these last approaches are a more ambitious challenge, recent works have shown promising expectations [3, 11]. In the framework of extractive approaches most systems are based on unsupervised learning models. This is the case of Latent Semantic Analysis (LSA) [7], or graph-based [4]. Other systems are based on supervised methods such as Recurrent Neural Networks [3], Conditional Random Fields (CRFs) [13], or Support Vector Machine (SVM) [5].

The organization of evaluation competitions has been an important help for the development of this area. This is the case of DUCFootnote 1 and TACFootnote 2 conferences. They have become a forum to compare the different approaches. To do this, some evaluation corpora have been developed that can be used not only for test purposes but also for training models. Some of the most popular corpora in summarization tasks are the corpus used in DUC and the CNN/DailyMail corpus. This last corpus has widely used for learning models in Neural Networks approaches [3].

Other authors have explored the summarization considering audio documents as input [6]. This task has the additional problems of dealing with different kinds of errors, as speech recognition errors and errors in punctuation of sentences. Moreover, some expressions that appear due to spontaneous speech characteristics must be specifically processed since they could be not relevant for the summary.

In this work, we present an extractive approach to document summarization based on unsupervised techniques, in particular Latent Dirichlet Allocation (LDA) [2]. This approach can be considered as topic-based because some topics can be automatically detected and used to determine the most salient sentences according to the topics that appear in the document. Another issue of this work is that we have addressed the problem of summarization of TV programs, in particular a magazine of news. Some characteristics of this task generate specific challenges to the summarization problem. Apart from the speech recognition problems, that are not considered in this work, the most interesting problem is that this kind of programs have a very variable structure, and usually many topics of different duration are present in them. We have studied two strategies of summarization: in the first one, the transcription of the program is the input to the summarization system, and in the second one, a preprocess of segmentation of the program is done, and from the concatenation of the summaries of each segment the final summary is obtained. We have performed some experiments on Spanish TV programs in order to study the behavior of the proposed techniques.

The paper is organized as follows. In Sect. 2, the different methodologies developed are described. In Sect. 3, a description of the system architecture is presented. In Sect. 4, we show the characteristics of the corpus. In Sect. 5, we present the experimental results, and in Sect. 6, the conclusions and future works are presented.

2 System Description

Given a document, considered as a set of sentences, the objective of an extractive summarization technique consists of assigning weights to the sentences, that represent the relevance of them. From this ranked set of sentences the system selects the first ones in order to build the summary.

2.1 Latent Semantic Analysis

Many unsupervised summarization systems are based on LSA. This technique permits to extract the representative sentences for the automatically detected topics in the documents. This is done applying the singular value decomposition of word-sentences matrices. That is, given the word-sentence matrix C the Singular Value Decomposition generates the U, \(\varSigma \), and \(V^{T}\) matrices, where \(V^{T}\) represents the association of underlying topics to sentences.

$$\begin{aligned} C = U\varSigma V^{T} \end{aligned}$$

From this decomposition there are different ways of assigning weights to sentences and then selecting those ones to appear in the summary. Some of them are based on the most salient sentence for each topic, others are based on the combination of the results of the matrix decomposition. We have chosen the Cross method that permits to extract more than one sentence associated to the most important topics [12].

2.2 Latent Dirichlet Allocation

Another way for discovering hidden topics in documents is the LDA approach. This methodology has been successfully used for topic identification, and can also be used for summary purposes. The basic idea is that documents are represented as random mixtures over latent topics, where each topic is characterized by a distribution over words. LDA assumes a generative process for each document in a corpus, given the a-priori parameters \(\alpha \) and \(\beta \), that characterize the probabilistic distributions. It assumes that for each word in a document, a topic is chosen given the multinomial distribution of topics, and then a word is chosen given multinomial probability of words conditioned by the selected topic. In order to use LDA, it is necessary to compute the posterior distribution of the hidden variables given a document, and to do this, one of the most popular approach is the Gibbs Sampling. Once the process is done for a fixed number of topics, two matrices are obtained: one of them represents the probability that a concept appears in a document, and the other one represents the probability that a word belongs to a topic (word-topic matrix).

Once these matrices have been obtained, we used the word-topic matrix to assign a weight to each word in a sentence. From this information we obtain a sentence-topics matrix that is the input to an adaptation of the Cross method used in the LSA approach.

2.3 Document Segmentation

Sometimes, as in our case, the documents to be summarized are long and heterogeneous, that is, they are composed by different sections, each one focused on a different subject. For this reason it could be convenient to split the document in different pieces, that is know as topic segmentation.

The approach that we have developed consists of obtaining vector representations of two consecutive paragraphs and defining a distance between vectors to decide if they belong to the same or to different topics. Then, an overlapped sliding window of paragraphs across the document provides the distances between two pairs of consecutive paragraphs. That is, we calculate at the end of each sentence the distance between the previous n sentences and the following n sentences. The length of the sliding window is experimentally determined.

In order to represent the paragraphs a semantic-based approach was done, in particular a Word2vec representation [10]. To do this, it was necessary to learn the Word2vec values from a large corpus. This was done from Wikipedia articles in Spanish.

Once the word representation was obtained, the way to represent the paragraphs was done by the addition of vectors of the words contained in them. The measure used to determine the distance between consecutive paragraphs was the cosine distance.

3 System Architecture

We have explored different approaches to the problem of summarization. Figure 1 shows the architecture of the first system. In it, the documents are the input to the LSA or LDA processes, and the matrix obtained is the input for the Cross method process.

Fig. 1.
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Architecture of the system.

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Fig. 2.
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Architecture of the system with a previous topic segmentation phase.

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Figure 2 shows the architecture for the summarization system when a previous phase of topic segmentation is performed. That is, first of all, the documents are segmented, and each segment is summarized. Then, a concatenation of this topic-dependent summaries is performed in order to generate the final summary.

4 Corpus Description

The corpus consists of seven Spanish TV programs of news including some miscellaneous topics, such as music, gastronomy, culture, etc. We used the correct transcriptions of the speech, in particular the screenplay of the program presenter. It should be noted that the structure of these programs is very heterogeneous. Sometimes a sequence of short news, one or two sentences, of different topics is followed by a long sequence of sentences related to one topic (for example a musical group that presents a new disc, even including interviews with the musicians). Some characteristics of this corpus are shown in Table 1. In order to evaluate the results, a summary of a 20% of the original document was performed for each document. They were manually built by an expert.

Table 1. Corpus characteristics.
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5 Experiments

Two series of experiments were done. The first one consisted in the application of both methodologies, LSA and LDA to the set of documents, and the second one was the application of the same methodologies with the previous topic segmentation process.

Table 2. Evaluation using LDA.
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We have used different ROUGE [8] measures to evaluate the summaries. The ROUGE metrics include: the ROUGE-n that measures the overlap of n-grams between the system and reference summaries, the ROUGE-L based on the Longest Common Subsequence (LCS), the ROUGE-W that is a Weighted LCS-based statistic, the ROUGE-S that is a skip-bigram based co-occurrence statistic, and finally, the ROUGE-SU that is a skip-bigram plus unigram-based co-occurrence statistic. The most widely used in the literature are the ROUGE-1, ROUGE-2, and the ROUGE-L.

The results of applying LDA and LSA directly to the transcriptions of the programs are shown in Tables 2 and 3 respectively. Results show that both methods have a good behavior and there is not a relevant difference between them. This can be explained by the fact that both approaches are based on the underlying topics of the documents, although each one of them has its particular way to model the semantics of the document.

Table 3. Evaluation using LSA.
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Table 4. Evaluation using LDA when a previous topic segmentation is done.
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Tables 4 and 5 show the results when a previous segmentation was done. The \(p_{k}\) value [1] of the segmentation was 0.59. It should be noted that the systems with a previous segmentation do not outperform the direct application of the proposed methodologies to the whole document. This could be explained by the fact that the topic segmentation approach is based on a decoupled architecture. That kind of decoupled architecture is very sensitive to the errors in the first phase of the process. This way the errors are transmitted to the following phases, the summarization in our case.

Table 5. Evaluation using LSA when a previous topic segmentation is done.
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6 Conclusions

In this paper we have presented an approach to summarization of Spanish TV programs. It is based on unsupervised methods, and it is specially oriented to documents with heterogeneous structures, that is, documents that contain many topics with very different durations. Two approaches based on underlying topic detection have been explored. The first one consists in the application of the methods directly to the document and the second one has a previous phase of topic segmentation. Results show that both approaches provide good results, and they have a similar behavior.

As future work, we will try to improve the segmentation based approach developing some mechanisms to transmit more than one segmentation hypothesis to the summarization phase. This way, the errors generated by the first phase could be recovered during the summarization process. It can be also interesting to develop another way to combine the summaries of the detected segments, instead of a straight forward concatenation of them.