acl acl2011 acl2011-156 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Jui-Yu Weng ; Cheng-Lun Yang ; Bo-Nian Chen ; Yen-Kai Wang ; Shou-De Lin
Abstract: This paper presents a system to summarize a Microblog post and its responses with the goal to provide readers a more constructive and concise set of information for efficient digestion. We introduce a novel two-phase summarization scheme. In the first phase, the post plus its responses are classified into four categories based on the intention, interrogation, sharing, discussion and chat. For each type of post, in the second phase, we exploit different strategies, including opinion analysis, response pair identification, and response relevancy detection, to summarize and highlight critical information to display. This system provides an alternative thinking about machinesummarization: by utilizing AI approaches, computers are capable of constructing deeper and more user-friendly abstraction. 1
Reference: text
sentIndex sentText sentNum sentScore
1 tw , , , , Abstract This paper presents a system to summarize a Microblog post and its responses with the goal to provide readers a more constructive and concise set of information for efficient digestion. [sent-4, score-0.665]
2 In the first phase, the post plus its responses are classified into four categories based on the intention, interrogation, sharing, discussion and chat. [sent-6, score-0.633]
3 For each type of post, in the second phase, we exploit different strategies, including opinion analysis, response pair identification, and response relevancy detection, to summarize and highlight critical information to display. [sent-7, score-0.924]
4 Take summarization for example, a Microblog user usually has to browse through tens or even hundreds of posts together with their responses daily, therefore it can be beneficial if there is an intelligent tool assisting summarizing those information. [sent-10, score-1.187]
5 Automatic text summarization (ATS) has been investigated for over fifty years, but the majority of the existing techniques might not be appropriate for Microblog write-ups. [sent-11, score-0.194]
6 For instance, a popular kind of approaches for summarization tries to identify a subset of information, usually in sentence form, from longer pieces of writings as summary (Das and Martins, 2007). [sent-12, score-0.313]
7 Below we first describe some special characteristics that deviates the Microblog summarization task from general text summarization. [sent-14, score-0.234]
8 Unlike normal blogs, there is a strict limitation on the number of characters for each post (e. [sent-17, score-0.161]
9 At least three different types of posts are observed in Microblogs, expressing feeling, sharing information, and asking questions. [sent-25, score-0.594]
10 Consequently, using one mold to fit all types of Microblog posts is not sufficient. [sent-29, score-0.457]
11 Different summarization schemes for posts with different purposes are preferred. [sent-30, score-0.651]
12 Posts and responses in Microblogs are more similar to a multi-persons dialogue corpus. [sent-32, score-0.472]
13 Sometimes, the topic of discussion at the end of the thread is totally unrelated to that of the post. [sent-35, score-0.137]
14 1c 12 S0y1s1te Amss Doecmiaotinosntr faotiron Cos,m papguetast 1io3n3a–l1 L3in8g,uistics This paper introduces a framework that summarizes a post with its responses. [sent-38, score-0.184]
15 Motivated by the abovementioned characteristics of Microblogs, we plan to use a two-phase summarization scheme to develop different summarization strategies for different type of posts (see Figure 1). [sent-39, score-0.917]
16 In the first phase, a post will be automatically classified into several categories including interrogation, discussion, sharing and chat based on the intention of the users. [sent-40, score-0.479]
17 In the second phase, the system chooses different summarization components for different types of posts. [sent-41, score-0.217]
18 Tactically, we argue that it is possible to integrate post-intention classification, opinion analysis, response relevancy and response-pair mining to create an intelligent summarization framework for Microblog posts and responses. [sent-51, score-1.23]
19 We also found that the content features are not as useful as the temporal or positional features for text mining in Microblog. [sent-52, score-0.153]
20 It is possible to go beyond the literal meaning of summarization to exploit advanced text mining methods to improve the quality and usability of a summarization system. [sent-55, score-0.436]
21 2 Summarization Framework riments and Expe- Below we discuss our two-phase summarization framework and the experiment results on each individual component. [sent-56, score-0.217]
22 Our observation is that Microblog posts can have different purposes. [sent-58, score-0.457]
23 The Interrogation posts are questions asked in public with the hope to obtain some useful answers from friends or other users. [sent-60, score-0.484]
24 The responses might serve the purpose for clarification or, even worse, have nothing to do with the question. [sent-62, score-0.439]
25 Hence we believe the most appropriate summarization process for this 134 kind of posts is to find out which replies really respond to the question. [sent-63, score-0.779]
26 We created a response relevance detection component to serve as its summarization mechanism. [sent-64, score-0.609]
27 The Sharing posts are very frequently observed in Microblog as Microbloggers like to share interesting websites, pictures, and videos with their friends. [sent-65, score-0.457]
28 We introduce the opinion analysis component that provides the analysis on whether the information shared is recommended by the respondents. [sent-68, score-0.128]
29 We also observe that some posts contain characteristics of both Interrogation and Sharing. [sent-69, score-0.497]
30 We create a category named Discussion for these posts, and apply both response ranking and opinion analysis engines on this type of posts. [sent-71, score-0.401]
31 Finally, there are posts which simply act as the solicitation for further chat. [sent-72, score-0.457]
32 This kind of posts can sometimes involve multiple persons and the topic may gradually drift to a different one. [sent-75, score-0.567]
33 We believe the plausible summarization strategy is to group different messages based on their topics. [sent-76, score-0.301]
34 Therefore for Chat posts, we designed a response pair identification system to accomplish such goal. [sent-77, score-0.396]
35 We group the related responses together for display, and the number of groups represents the number of different topics in this thread. [sent-78, score-0.488]
36 Figure 1 shows the flow of our summarization … framework. [sent-79, score-0.194]
37 When an input post with responses comes in, the system first determines its intention, based on which the system adopts proper strategies for summarization. [sent-80, score-0.645]
38 1 Post Intention Classification This stage aims to classify each post into four categories, Interrogation, Sharing, Discussion, and Chat. [sent-83, score-0.161]
39 One tricky issue is that the Discussion label is essentially a combination of interrogation and sharing labels. [sent-84, score-0.371]
40 The system first checks whether the posts contains URLs or pointers to files, then uses a binary classifier to determine whether the post is interrogative. [sent-89, score-0.681]
41 For the experiment, we manually annotate 6000 posts consisting of 1840 interrogation, 2002 sharing, 1905 chat, and 254 discussion posts. [sent-90, score-0.499]
42 We train a 6-gram language model as the binary interrogation classifier. [sent-91, score-0.234]
43 Then we integrate the classifier into our system and test on 6000 posts to obtain a testing accuracy of 82. [sent-92, score-0.52]
44 The system classifies responses into 3 categories, positive, negative, and neutral. [sent-97, score-0.429]
45 First of all, we train a binary classifier to determine if a post or a reply is opinionative. [sent-99, score-0.242]
46 If the answer is yes, we then use another binary classifier to decide if the opinion is positive or negative. [sent-101, score-0.182]
47 For polarity test, we exploit the built-in emoticons in Plurk to automatically extract posts with positive and negative opinions. [sent-106, score-0.586]
48 We collect 10,000 positive and 10,000 negative posts as training data to train a language model of Naïve Bayes classifier, and evaluate on manually annotated data of 3121 posts, with 1624 positive and 1497 negative to obtain accuracy of 0. [sent-107, score-0.605]
49 3 Response Pair Identification Conversation in micro-blogs tends to diverge into multiple topics as the number of responses grows. [sent-110, score-0.482]
50 Sometimes such divergence may result in responses that are irrelevant to the original post, thus creating problems for summarization. [sent-111, score-0.406]
51 Furthermore, because the messages are usually short, it is difficult to identify the main topics of these dialoguelike responses using only keywords in the content for summarization. [sent-112, score-0.609]
52 A Response Pair is a pair of responses that the latter specifically responds to the former. [sent-114, score-0.427]
53 Based on those pairs we can then form clusters of messages to indicate different group of topics and mesFeature Description Weight Backward RefeLatter response content 0. [sent-115, score-0.597]
54 055 rencing contains former responder’s display name Forward RefeFormer response contains 0. [sent-116, score-0.428]
55 018 rencing of user latter response’s author’s name user name Response position Number of responses in 0. [sent-117, score-0.559]
56 13 difference between responses Content similarity Contents’ cosine similari- 0. [sent-118, score-0.452]
57 Looking at the content of micro-blogs, we observe that related responses are usually adjacent to each other as users tend to closely follow whether their messages are responded and reply to the responses from others quickly. [sent-123, score-1.058]
58 Therefore besides content features, we decide to add the temporal and ordering features (See Table 1) to train a classifier that takes a pair of messages as inputs and return whether they are related. [sent-124, score-0.231]
59 By identifying the response pairs, our summarization system is able to group the responses into different topic clusters and display the clusters separately. [sent-125, score-1.114]
60 For experiment, the model is trained using LIBSVM (Chang and Lin, 2001) (RBF kernel) with 6000 response pairs, half of the training set positive and half negative. [sent-127, score-0.434]
61 Responses with @user_name string in the content are matched with earlier responses by the author, user_name. [sent-129, score-0.47]
62 Based on the learned weights of the features, we observe that content feature is not very useful in determining the response pairs. [sent-130, score-0.412]
63 We also have noticed that there is high correlation between the responses relatedness and the number of other responses between them. [sent-132, score-0.812]
64 For example, users are less likely to respond to a response if there have been many replies about this response already. [sent-133, score-0.801]
65 Statistical analy- sis on positive training data shows that the average number of responses between related responses is 2. [sent-134, score-0.855]
66 We train the classifier using 6000 automaticallyextracted pairs of both positive and negative instances. [sent-136, score-0.164]
67 The baseline model which uses only content similarity feature reaches only 45% in accuracy. [sent-140, score-0.117]
68 4 Response Relevance Detection For interrogative posts, we think the best summary is to find out the relevent responses as potential answers. [sent-142, score-0.55]
69 We introduce a response relevancy detection component for the problem. [sent-143, score-0.485]
70 Temporal and Positional Features A common assertion is that the earlier responses have higher probability to be the answers of the question. [sent-151, score-0.406]
71 Based on the learned weights, it is not surprising that most important feature is the position of the response in the response hierarchy. [sent-152, score-0.65]
72 Content Features We use the length of the message, the cosine similarity of the post and the responses, and the occurrence of the interrogative words in response sentences as content features. [sent-155, score-0.693]
73 Because the interrogation posts in Plurk are relatively few, we manually find a total of 382 positive and 403 negative pairs for training and use 10-fold cross validation for evaluation. [sent-156, score-0.84]
74 The baseline is to always select the first response as the only relevant answer. [sent-158, score-0.325]
75 3 System Demonstration In this section, we show some snapshots of our summarization system with real examples using Plurk dataset. [sent-162, score-0.217]
76 Given a query term, our system first returns several posts containing the query string under the search bar. [sent-166, score-0.48]
77 When one of the posts is selected, it will generate a summary according to the detected intention and show it in a pop-up frame. [sent-167, score-0.592]
78 For interrogative posts, we perform the response relevancy detection. [sent-172, score-0.525]
79 Figure 4 is an example of summary of an interrogative post. [sent-174, score-0.144]
80 We can see that responses other than the first and the last responses are filtered because they are less relevant to the question. [sent-175, score-0.812]
81 For sharing posts, the summary consists of two parts. [sent-176, score-0.184]
82 Then the system picks three responses from the majority group or one response from each group if there is no significant difference. [sent-178, score-0.834]
83 Figure 5 is an example that most friends of the user dfrag give positive feedback to the shared video link. [sent-179, score-0.159]
84 For discussion posts, we combine the response relevancy detection subsystem and the opinion analysis sub-system for summarization. [sent-180, score-0.578]
85 The former first eliminates the responses that are not likely to be the answer of the post. [sent-181, score-0.429]
86 The latter then generates a summary for the post and relevant responses. [sent-182, score-0.208]
87 For chat posts, we apply the response pair identification component to generate the summary. [sent-184, score-0.467]
88 In the example, Figure 6, the original Plurk post is about one topic while the responses diverge to one 137 Figure 5. [sent-185, score-0.637]
89 Our system clearly separates the responses into multiple groups. [sent-189, score-0.429]
90 The users no longer have to read interleaving responses from different topics and guess which topic group a response is referring to. [sent-191, score-0.893]
91 We found only one work that discusses about the issues of summarization for Microblogs (Sharifi et al. [sent-193, score-0.194]
92 Their goal, however, is very different from ours as they try to summarize multiple posts and do not consider the responses. [sent-195, score-0.504]
93 They are essentially trying to solve a multi-document summarization problem while our problem is more similar to short dialog summarization because the dialogue nature of Microblogs is one of the most challenging part that we tried to overcome. [sent-197, score-0.454]
94 In dialogue summarization, many researchers have pointed out the importance of detecting response pairs in a conversation. [sent-198, score-0.42]
95 Zhou and Hovy (2005) concentrates on summarizing dialogue-style technical internet relay chats using supervised learning methods. [sent-202, score-0.127]
96 Zhou further clusters chat logs into several topics and then extract some essential response pairs to form summaries. [sent-203, score-0.495]
97 Due to the intrinsic difference between the writing styles of Microblog and other online sources, our experiments show that the content feature is not as useful as the position and temporal features. [sent-207, score-0.13]
98 Our system uses an effective strategy to summarize the post/response by first determine the intention and then perform different analysis depending on the post types. [sent-210, score-0.319]
99 By utilizing text mining and analysis techniques, computers are capable of providing more intelligent summarization than information condensation. [sent-212, score-0.292]
100 Digesting virtual geek culture: The summarization of technical internet relay chats, in Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL 2005). [sent-248, score-0.232]
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