acl acl2012 acl2012-55 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Wen Chan ; Xiangdong Zhou ; Wei Wang ; Tat-Seng Chua
Abstract: We present a novel answer summarization method for community Question Answering services (cQAs) to address the problem of “incomplete answer”, i.e., the “best answer” of a complex multi-sentence question misses valuable information that is contained in other answers. In order to automatically generate a novel and non-redundant community answer summary, we segment the complex original multi-sentence question into several sub questions and then propose a general Conditional Random Field (CRF) based answer summary method with group L1 regularization. Various textual and non-textual QA features are explored. Specifically, we explore four different types of contextual factors, namely, the information novelty and non-redundancy modeling for local and non-local sentence interactions under question segmentation. To further unleash the potential of the abundant cQA features, we introduce the group L1 regularization for feature learning. Experimental results on a Yahoo! Answers dataset show that our proposed method significantly outperforms state-of-the-art methods on cQA summarization task.
Reference: text
sentIndex sentText sentNum sentScore
1 sg , , Abstract We present a novel answer summarization method for community Question Answering services (cQAs) to address the problem of “incomplete answer”, i. [sent-7, score-0.79]
2 In order to automatically generate a novel and non-redundant community answer summary, we segment the complex original multi-sentence question into several sub questions and then propose a general Conditional Random Field (CRF) based answer summary method with group L1 regularization. [sent-10, score-2.037]
3 Specifically, we explore four different types of contextual factors, namely, the information novelty and non-redundancy modeling for local and non-local sentence interactions under question segmentation. [sent-12, score-0.533]
4 To further unleash the potential of the abundant cQA features, we introduce the group L1 regularization for feature learning. [sent-13, score-0.296]
5 1 Introduction Community Question and Answering services (cQAs) have become valuable resources for users to pose questions of their interests and share their knowledge by providing answers to questions. [sent-16, score-0.522]
6 Answers, a resolved question often gets more than one answers and a “best answer” will be chosen by the asker or voted by other community participants. [sent-21, score-0.67]
7 d I itn performs very well in simple factoid QA settings, where the answers to factoid questions often relate to a single named entity like a person, time or location. [sent-24, score-0.613]
8 That is, such question often comprises several sub questions in specific contexts and the asker wishes to get elaborated answers for as many aspects of the question as possible. [sent-26, score-1.189]
9 In which case, the single best answer that covers just one or few aspects may not be a good choice (Liu et al. [sent-27, score-0.606]
10 , 2008), the use of a single best answer often misses valuable human generated information contained in other answers. [sent-31, score-0.637]
11 (2008) reported that no more than 48% of the 400 best answers were indeed the unique best answers in 4 most popular Yahoo! [sent-33, score-0.63]
12 Gums that bleed could be a sign of a more serious issue like leukemia, an infection, gum disease, a blood disorder, or a vitamin deficiency. [sent-58, score-0.316]
13 your gum Table 1: An example of question with incomplete answer problem from Yahoo! [sent-61, score-1.01]
14 In fact, it is often the case, that a complex multi-sentence question could be answered from multiple aspects by different people focusing on different sub questions. [sent-66, score-0.378]
15 Therefore we address the incomplete answer problem by developing a novel summarization technique taking different sub questions and contexts into consideration. [sent-67, score-1.178]
16 Specifically we want to learn a concise summary from a set of corresponding answers as supplement or replacement to the “best answer”. [sent-68, score-0.375]
17 Various textual and non-textual question answering features are exploited in the work. [sent-71, score-0.328]
18 Second, we propose a group L1-regularization approach in the CRF model for automatic optimal feature learning to unleash the potential of the features and enhance the performance of answer summarization. [sent-72, score-0.794]
19 First we define a complex multi-sentence question as a question with the following properties: Definition: A complex multi-sentence question is one that contains multiple sub-questions. [sent-82, score-0.684]
20 In the cQAs scenario a question often consists of one or more main question sentences accompany by some context sentences described by askers. [sent-83, score-0.516]
21 We treat the original question and context as a whole single complex multi-sentence question and obtain the sub questions by question segmentation. [sent-84, score-1.039]
22 We study the issues of similarity threshold and the minimal number of stars empirically in the experimental section and show that they are useful in identifying questions with the incomplete answer problem. [sent-86, score-0.995]
23 2 Related Work There exist several attempts to alleviate the answer completeness problem in cQA. [sent-88, score-0.577]
24 One of them is to segment the multi-sentence question into a set of sub-questions along with their contexts, then sequentially retrieve the sub questions one by one, and return similar questions and their best answers (Wang et al. [sent-89, score-1.103]
25 On general problem of cQA answer summarization, Liu et al. [sent-92, score-0.606]
26 (2008) manually classified both questions and answers into different taxonomies and applied clustering algorithms for answer summarization. [sent-93, score-1.068]
27 Through exploiting metadata, Tomasoni and Huang(2010) introduced four characteristics (constraints) of summarized answer and combined them in an additional model as well as a multiplicative model. [sent-95, score-0.643]
28 However there is no previous work that explores the complex multi-sentence question segmentation and its contextual modeling for community answer summarization. [sent-100, score-1.02]
29 Some other works examined the evaluation of the quality of features for answers extracted from cQA services (Jeon et al. [sent-101, score-0.318]
30 (2010), a large number of features extracted for 584 predicting asker-rated quality of answers was evaluated by using a logistic regression model. [sent-105, score-0.318]
31 However, to the best of our knowledge, there is no work in evaluating the quality of features for community answer summarization. [sent-106, score-0.702]
32 In our work we model the feature learning and evaluation problem as a group L1 regularization problem (Schmidt , 2010) on different feature groups. [sent-107, score-0.301]
33 1 Conditional Random Fields We utilize the probabilistic graphical model to solve the answer summarization task, Figure 1gives some illustrations, in which the sites correspond to the sentences and the edges are utilized to model the interactions between sentences. [sent-109, score-0.886]
34 Specifically, let x be the sentence sequence to all answers within a question thread, and y be the corresponding label se- µ quence. [sent-110, score-0.569]
35 2 cQA Features and Contextual Modeling In this section, we give a detailed description of the different sentence-level cQA features and the contextual modeling between sentences used in our model for answer summarization. [sent-115, score-0.752]
36 Sentence-level Features Different from the conventional multi-document summarization in which only the textual features are utilized, we also explore a number of non-textual author related features (Shah et al. [sent-116, score-0.294]
37 Sentence Length: The length of the sentence in the answers with the stop words removed. [sent-119, score-0.374]
38 If a sentence is at the beginning or at the end of one answer, it might be a generation or viewpoint sentence and will be given higher weight in the summarization task. [sent-123, score-0.298]
39 Answer Length: The length of the answer to which the sentence belonged, again with the stop words removed. [sent-125, score-0.665]
40 Similarity to Question: Semantic similarity to the question and question context. [sent-136, score-0.506]
41 It imports the semantic information relevance to the question and question context. [sent-137, score-0.456]
42 Best Answer Star: The stars of the best answer received by the askers or voters. [sent-139, score-0.64]
43 Thumbs Up: The number of thumbs-ups the answer which contains the sentence receives. [sent-141, score-0.632]
44 Users are often used to support one answer by giving a thumbs up after reading some relevant or interesting information for their intentions. [sent-142, score-0.614]
45 Author Level: The level of stars the author who gives the answer sentence acquires. [sent-144, score-0.712]
46 Best Answer Rate: Rate of answers annotated as the best answer the author who gives the answer sentence receives. [sent-147, score-1.57]
47 Total Answer Number: The number of total answers by the author who gives the answer sentence. [sent-149, score-0.909]
48 Total Points: The total points that the author who gives the answer sentence receives. [sent-152, score-0.678]
49 Therefore, to explore the optimal combination of these features, we propose a group L1 regularization term in the general CRF model (Section 3. [sent-158, score-0.291]
50 This give rise to four contextual factors that we will explore for modeling the pairwise semantic interactions based on question segmentation. [sent-167, score-0.487]
51 Question sentence detection: every sentence in the original multi-sentence question is classified into question sentence and non-question (context) sentence. [sent-172, score-0.621]
52 We compute the semantic similarity(Simpson and Crowe, 2005) between sentences or sub ques- Figure 1: Four kinds of the contextual factors are considered for answer summarization in our general CRF based models. [sent-176, score-1.134]
53 One answer sentence may related to more than one sub questions to some extent. [sent-178, score-0.987]
54 Thus, we define the replied question Qri as the sub question with the maximal similarity to sentence xi: Qri = argmaxQj sim(xi, Qj). [sent-179, score-0.803]
55 It is intuitive that different summary sentences aim at answering different sub questions. [sent-180, score-0.302]
56 Therefore, we design the following two contextual factors based on the similarity of replied questions. [sent-181, score-0.341]
57 Dissimilar Replied Question Factor: Given two answer sentences xi , xj and their corresponding replied questions Qri, Qrj. [sent-182, score-1.026]
58 If the similarity2 of Qri and Qrj is below some threshold τlq, it means that xi and xj will present different viewpoints to answer different sub questions. [sent-183, score-0.881]
59 In this case, it is likely that xi and xj are both summary sentences; we ensure this by setting the contextual factor cf1 with a large value of exp ν, where ν is a positive real constant often assigned to value 1; otherwise we set cf1 to exp − ν for penalization. [sent-184, score-0.357]
60 586 swer sentences xi , xj and their corresponding replied questions Qri, Qrj. [sent-186, score-0.503]
61 If the similarity of Qri and Qrj is above some upper threshold τuq, this means that xi and xj are very similar and likely to provide similar viewpoint to answer similar questions. [sent-187, score-0.82]
62 Therefore, we propose the following two kinds of contextual factors for selecting the answer sentences in the CRF model. [sent-191, score-0.806]
63 Notice that this penalty term is indeed a L(1, 2) regularization because in every particular group we normalize the parameters in L2 norm while the weight of a whole group is summed in L1 form. [sent-198, score-0.412]
64 The first two terms of Equation 6 measure the difference between the empirical and the model expected values of feature j in group g, while the third term is the derivative of group L1 priors. [sent-220, score-0.305]
65 Although it is an approximation of the exact inference, we will see that it works well for our answer summarization task in the experiments. [sent-224, score-0.726]
66 1 Dataset To evaluate the performance of our CRF based answer summarization model, we conduct experiments on the Yahoo! [sent-226, score-0.726]
67 Our original dataset contains 1,300,559 questions and 2,770,896 answers in ten taxonomies from Yahoo! [sent-231, score-0.491]
68 After filtering the questions which have less than 5 answers and some trivial factoid questions using the features by (Tomasoni and Huang, 2010) , we reduce the dataset to 55,132 questions. [sent-233, score-0.789]
69 From this sub-set, we next select the questions with incomplete answers as defined in Section 2. [sent-234, score-0.588]
70 Specifically, we select the questions where the average similarity between the best answer and all sub questions is less than 0. [sent-236, score-1.216]
71 6 or when the star rating of the best answer is less than 4. [sent-237, score-0.671]
72 To evaluate the effectiveness of this method, we randomly choose 400 questions in the filtered dataset and invite 10 graduate candidate students (not in NLP research field) to verify whether a question suffers from the incomplete answer problem. [sent-239, score-1.107]
73 We consider the questions as the “incomplete answer questions” only when they are judged by both members in a group to be the case. [sent-241, score-0.9]
74 As a result, we find that 360 (90%) of these questions indeed suffer from the incomplete answer problem, which indicates that our automatic detection method is efficient. [sent-242, score-0.879]
75 This randomly selected 400 questions along with their 2559 answers are then further manually summarized for evaluation of automatically generated answer summaries by our model in experiments. [sent-243, score-1.134]
76 In our experiments, we also compare the precision, recall and F1 score in the ROUGE-1, ROUGE2 and ROUGE-L measures (Lin , 2004) for answer summarization performance. [sent-249, score-0.798]
77 For linear CRF system, we use all our textual and non-textual features as well as the local (exact previous and next) neighborhood contextual factors instead of the features of (Shen et al. [sent-254, score-0.329]
78 Table 2 shows that our general CRF model based on question segmentation with group L1 regularization out-performs the baselines significantly in all three measures (gCRF-QS-l1 is 13. [sent-261, score-0.591]
79 This is mainly because we have divided the question into several sub questions, and the system is able to select more novel sentences than just treating the original multi-sentence as a whole. [sent-273, score-0.408]
80 In addition, when we replace the default L2 regularization by the group L1 regularization for more efficient feature weight learning, we obtain a much better performance in 589 precision while not sacrificing the recall measurement statistically. [sent-274, score-0.47]
81 Therefore, the im- provements in these measures are more encouraging than those of the average classification accuracy for answer summarization. [sent-280, score-0.616]
82 From the viewpoint of ROUGE measures we observe that our question segmentation method can enhance the recall of the summaries significantly due to the more fine-grained modeling of sub questions. [sent-281, score-0.527]
83 We also find that the precision of the group L1 regularization is much better than that of the default L2 regularization while not hurting the recall significantly. [sent-282, score-0.405]
84 In general, the experimental results show that our proposed method is more effective than other baselines in answer summarization for addressing the incomplete answer problem in cQAs. [sent-283, score-1.428]
85 Figure 2: The accumulated weight of each site feature group in the group L1-regularization to our Yahoo! [sent-284, score-0.304]
86 To see how much the different textual and non-textual features contribute to community answer summarization, the accumulated weight of each group of sentence-level features5 is presented in Figure 2. [sent-289, score-0.858]
87 The main reasons that the feature 7 (Similarity to Question) has low contribution is that we have utilized the similarity to question in the contextual factors, and this similarity feature in the single site becomes redundant. [sent-291, score-0.548]
88 3 An Example of Summarized Answer To demonstrate the effectiveness of our proposed method, Table 4 shows the generated summary of the example question which is previously illustrated in Table 1 in the introduction section. [sent-297, score-0.317]
89 The best answer available in the system and the summarized answer generated by our model are compared in Table 4. [sent-298, score-1.249]
90 It is found that the summarized answer contains more valuable information about the original multi- sentence question, as it better answers the reason of blooding and offers some solution for it. [sent-299, score-1.046]
91 Storing and indexing this summarized answer in question archives should provide a better choice for anteeth swer reuse in question retrieval of cQAs. [sent-300, score-1.153]
92 Gums that bleed could be a sign of a more serious issue like leukemia, an infection, gum disease, a blood disorder, or a vitamin deficiency. [sent-310, score-0.316]
93 Table 4: Summarized answer by our general CRF based model for the question in Table 1. [sent-312, score-0.834]
94 6 Conclusions We proposed a general CRF based community swer summarization method an- to deal with the in- complete answer problem for deep understanding of complex multi-sentence questions. [sent-313, score-0.873]
95 Our main contributions are that we proposed a systematic way for modeling semantic contextual interactions between the answer sentences based on question segmentation and we explored both the textual and nontextual answer features learned via a group L1 regularization. [sent-314, score-1.808]
96 We showed that our method is able to achieve significant improvements in performance of answer summarization compared to other baselines and previous methods on Yahoo! [sent-315, score-0.754]
97 We planed to extend our proposed model with more advanced feature learning as well as enriching our summarized answer with more available Web resources. [sent-317, score-0.679]
98 Retrieving answers from frequently asked questions pages on the web. [sent-373, score-0.491]
99 Understanding and summarizing answers in community-based question answering services. [sent-405, score-0.547]
100 Statistical machine translation for query expansion in answer retrieval. [sent-414, score-0.577]
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